Fear of death is often pathologised, yet little research has examined its unique links to different aspects of an individual’s life. This study investigated the unique associations among fear of death, psychological distress, mindfulness, compassion, life limiting illness, spirituality, meaning in life, and self-esteem using network analysis. It also explored the potential influence of meditation and psychedelics.
Method
A total of 486 community-based participants including university students completed a series of online surveys, which comprised a sociodemographic questionnaire and eight self-report rating scales. A Bayesian Gaussian Graphical Model was used to estimate nondirectional associations between variables, while a Directed Acyclic Graph depicted probabilities of directional links.
Results
The directed network revealed the central role of fear of another's death and its direct influence on other facets of death anxiety, life limiting illness, mindfulness, meaning in life, and subsequently spirituality and other variables in the network. The remaining facets of death anxiety had no significant influence on any wellbeing-related variables. This study empirically expanded upon the Terror Management Theory and found that self-esteem may not buffer death anxiety as previously proposed. Secondary analyses found that psychedelic use and meditation experience correlated with lower death anxiety, but these findings require replication due to small samples between groups.
Conclusions
These findings reframe assumptions about death anxiety by revealing its specific effects and avenues for promoting wellness surrounding mortality. In addition, they provide empirical support for Buddhist teachings, which emphasise the awakening effect of directly realising mortality. These results suggest that death anxiety could facilitate existential growth, contributing to the body of literature that highlights its potential positive effects.
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Death is the only certainty of the human experience. Humans are the only species known to understand and contemplate the inevitability of death. The uncertainty of what death itself is and whether something follows affects our human experience as we attempt to make sense of it through philosophical ideas, behaviours, and customs (Anālayo et al., 2022). Our awareness of mortality gives rise to existential questions in our search for the meaning of life. Some spend years denying and avoiding the subject out of fear and anguish (Furer & Walker, 2008; Lehto & Stein, 2009), while others turn to philosophical frameworks (such as religion) offering explanations of death, moral conduct, and the purpose of our existence (Greenberg et al., 1986). The influence of an individual's fear regarding their own mortality and the mortality of others is a subject of debate within psychological literature due to the varied implications it holds for one's life and well-being.
Death anxiety, commonly known as fears of death and dying, encompasses increased apprehension linked to the awareness of one's own and others' mortality, along with the uncertainty surrounding the cessation of existence (Juhl & Routledge, 2016). This emotional reaction is acknowledged in psychological literature as a prevalent facet of the human experience, wherein individuals contend with the impermanence of life and uncertainties about what transpires after death (Lehto & Stein, 2009). Fears of death and dying can revolve around the notion of ceasing to exist (death) and the process of losing oneself and others (dying). The fear of death is purported to be present from birth on an instinctual level, protecting us from danger to ensure survival (Menzies & Menzies, 2020). However, conceptual understandings of death and the associated fears appear to be learned through experiences throughout life (Firestone, 1994). For example, the motivation for the Buddha to leave his family and begin his quest for awakening arose from his realisation of the inevitability of being subject to old age, disease, and death (Anālayo, 2017).
It is thought that one’s reaction to and ability to cope with death anxiety determine its effect on well-being (Greenberg et al., 1986). Making sense of grief and impermanence in a positive way can lead to a deeper sense of meaning in life, which may enhance spirituality and, in turn, promote greater well-being (Koenig, 2004; Neimeyer et al., 2002). However, negatively processing the concept of impermanence and fearing death can lead to pathological concerns (Iverach et al., 2014). In clinical settings, fear of death and dying is commonly referred to as “thanatophobia” and appears to have transdiagnostic value, presenting itself across a range of mental health conditions (Özgüç et al., 2021). However, the direct impact of the fear of death and dying in the general population has yet to be definitively determined.
Recently, the COVID-19 pandemic has heightened awareness of mortality, creating a surge of fear surrounding death and dying (Shehla, 2020). High levels of death anxiety have been associated with numerous adverse health outcomes, including life dissatisfaction, low resilience, psychosocial distress, declines in physical function, and impaired ego integrity (Semenova & Stadtlander, 2016). People are sometimes forced to confront the inevitability of death and dying. For example, death anxiety is prevalent among cancer patients, which has been found to impact physical, psychological, and social domains in life (Soleimani et al., 2016). Studies have identified high prevalence rates of clinically significant anxiety (14%) and depression (24%) among palliative cancer care patients (Wilson et al., 2007). Additionally, Nerum et al. (2006) identified that 74% of pregnant woman planning for a caesarean section were afraid of dying during pregnancy or birth. This underscores the extent to which fears of death and dying can arise if untreated in a clinical setting.
Healthcare workers are frequently exposed to reminders of mortality, such as sickness, trauma, and violence. Consequently, it is not surprising that there are high rates of death anxiety among nurses and other healthcare workers (Nia et al., 2016). These job-related stressors increase death anxiety, which negatively affects empathetic responses and communication with patients (Thiemann et al., 2014). In addition, burnout from death anxiety is common in healthcare workers, especially hospice workers (Quinn-Lee et al., 2014). This underscores the potential challenges associated with elevated death anxiety among healthcare professionals and its consequential impact on patient care.
Considering cultural and demographic factors is crucial for a comprehensive understanding of death anxiety, as these elements profoundly shape individuals' experiences and coping mechanisms. Cultural worldviews significantly influence mortality responses, with collectivist societies like Japan often showing greater concern for collective mortality and in-group duties, while individualistic cultures such as Australia tend to emphasise personal mortality and autonomous self-concepts (Kashima et al., 2004). Age also plays a pivotal role, with older adults potentially experiencing death anxiety differently due to increased mortality salience and life experiences (Maxfield et al., 2007). Socioeconomic status, educational background, and gender also contribute to diverse experiences of death anxiety, affecting access to resources and shaping individual perspectives on mortality (Soleimani et al., 2017).
One of the most prominent theories is Terror Management Theory (TMT), which proposes that awareness of the inevitability of death and dying (mortality salience) creates fear (terror) that motivates individuals to reduce their anxiety through various coping methods (management; Anālayo et al., 2022; Greenberg et al., 1986). TMT suggests two strategies to manage the anxiety stemming from mortality salience: connection to cultural worldviews and improving self-esteem. As individuals become aware of their mortality, they adopt beliefs, values, and societal norms that provide a sense of order, structure, and meaning in their lives. Connecting with a larger belief system offers direction, allowing individuals to structure their behaviour in a manner that aligns with these adopted beliefs. This connection enhances their sense of belonging and significance in the world, thereby reducing anxiety around their mortality (Greenberg et al., 1986). Self-esteem, on the other hand, refers to the evaluation of one’s worth and significance within their cultural worldview (Greenberg et al., 1986). Individuals determine their personal value based on two elements: faith in the cultural narrative that deems human life salient and meaningful and the assurance that they contribute meaningfully to this narrative, providing a sense of importance within the cultural framework.
Religious and spiritual beliefs play a significant role in the cultural worldview aspect of TMT. Religions offer a comprehensive system of beliefs that provide explanations about the meaning of life, death, and the afterlife, thereby reducing anxiety around death (Jong et al., 2018). Many religious beliefs promote the ideology of immortality, which is particularly appealing for managing the notion of death (Dechesne et al., 2003; Jong et al., 2018). Immortality can be identified in two forms: literal and symbolic (Dechesne et al., 2003). Literal immortality refers to the quest for continued existence after death (an afterlife), such as through beliefs in heaven, reincarnation, or the existence of souls. Symbolic immortality, on the other hand, is sought through identifications, achievements, and accomplishments that hold lasting cultural value, such as being a good parent, performing well in one’s occupation, and contributing to society (Dechesne et al., 2003; Jong et al., 2018).
While literal and symbolic immortality represent distinct quests, they serve a similar function: coping with the salience of mortality (Dechesne et al., 2003; Greenberg et al., 1986). However, the relationship between religiosity and managing death anxiety remains questionable. Despite substantial investigation, meta-analyses reveal only weak links, likely due to unaccounted moderating factors (Jong et al., 2018). The anxiety-buffering capacity of religiosity appears conditional; those low in faith may experience heightened death fears, while stronger adherence can mitigate worries through an increased perceived likelihood of an afterlife and self-esteem tied to salvation beliefs (Homans, 1941; Jong et al., 2018; Rababa et al., 2021). Ultimately, fully elucidating the multifaceted interplay of influences on death anxiety requires examining interactive effects among an array of physical, psychological, and cultural variables.
TMT emphasises the critical role self-esteem plays in mitigating death anxiety (Greenberg et al., 1986). The presence of death anxiety has been shown to predict psychological distress (White & Handal, 1991). More recent research supports the idea that self-esteem acts as a protective factor against the negative impact of death anxiety on well-being (Abeyta et al., 2014; Routledge, 2012; Routledge et al., 2010; Wisman & Heflick, 2016). This buffering effect has been demonstrated in both clinical and non-clinical populations. For example, spiritual care interventions have reduced death anxiety in clinical groups by increasing self-esteem (Akbari et al., 2021). However, in healthy populations, findings have been mixed, with some studies reporting no significant effects of self-esteem on mortality-related fears (Routledge, 2012). Nevertheless, TMT posits that bolstering self-esteem may help alleviate death anxiety in most cases.
Conversely, little research has investigated the positive effect death anxiety could have on self-esteem. Pyszczynski et al. (2004) emphasised that reminders of mortality led to increased self-esteem. With this in mind, accepting the inevitability of death and the accompanying fear could be associated with higher levels of self-esteem due to an increased evaluation of one’s worth and significance within their cultural context (Greenberg et al., 1986). Therefore, it is important to examine the network of relationships to demonstrate the potential positive or negative impact that the fear of death and dying could have on different aspects of well-being, such as self-esteem and psychological distress.
The psychological response to the inevitability of death and dying significantly shapes our emotions and behaviour in everyday life. Positively discerning and accepting the notion of one’s finite experience can lead to transformative experiences that enrich the meaning of life (Greenberg et al., 1986; Schmeichel & Martens, 2005) However, truly understanding the concept of death and dying is commonly avoided until old age (De Raedt et al., 2013). While much psychological literature frames fear of death and dying as pathological (Iverach et al., 2014), little research has focused on the potential benefits, such as increased meaning in life, appreciation for life, and spiritual development that can arise from an awareness of mortality. As noted by Anālayo (2016, p. 204), “Only once death has become a natural part of life will it be possible to go beyond the influence of existential fear and thereby come fully alive to life as it unfolds in the present moment.”
The study of death anxiety has a long history, with research identifying various factors that predict its severity. However, there is little research debunking the effect death anxiety has on numerous aspects of well-being. One prominent influence on death anxiety is mindfulness, defined as the nonjudgemental awareness of present-moment experiences, including raw internal and external phenomena, before they are shaped by conceptualisations and schemas (Baer et al., 2006; Medvedev et al., 2016). Multiple studies demonstrate that mindfulness-based interventions effectively reduce death-related fears in diverse contexts, including intensive care nurses (Xiang-Zi & Jia-yuan, 2023) and pregnant women (Askarizadeh et al., 2022). Indirect effects are also evidenced, e.g., heightened mindfulness is correlated with increased compassion (Lim et al., 2015) and meaning in life (Tan et al., 2023), both of which mediate lower death anxiety levels in elderly participants (Baharvandi et al., 2020; Dursun et al., 2022). Thus, when death anxiety is treated as a pathological concern, mindfulness appears to effectively reduce death-related fears. However, there is little research elucidating the impact of these fears on mindfulness. Therefore, the inter-relationships among mindfulness, death anxiety, and related variables need to be explored to determine the functions these phenomena have on well-being.
The relationship between meaning in life and death anxiety has been rigorously studied (Zhang et al., 2019). Steger and Frazier (2005) defined the concept of meaning in life as an individual's subjective sense of the significance of their life, rooted in the belief that their life holds purpose and is understandable. Meaning in life is indirectly linked to TMT through the notion of cultural worldview and self-esteem, both of which are related to meaning in life (Greenberg et al., 1986). Wheeler (2001) reported that more compassionate people experience an improvement in the meaning in their lives. Wong (2007) introduced the Meaning Management Theory (MMT), positing that humans are driven by two primary motivations: the instinct to survive and the quest to discover meaning and purpose in that survival journey. MMT proposes that embracing the inevitability of death is connected to living a meaning-filled life (Wong, 2007; Zhang et al., 2019). In addition, Juhl and Routledge (2016) found that conscious reminders of death made people who had less sense of meaning in life become more anxious about death, while those with a stronger sense of meaning in life did not experience heightened fears of death when reminded. Meaning in life also appears to have mediating relationships with self-esteem (Zhang et al., 2019), self-compassion (Suh & Chong, 2022), and mindfulness (Tan et al., 2023), which are subsequently associated with lower levels of death anxiety.
Nevertheless, few studies have investigated the potential influence of death anxiety on meaning in life. Perhaps, meaning in life is grounded in the understanding that life will eventually end. In essence, rather than taking life for granted or becoming overly attached to temporary pleasures, recognising the impermanence of existence encourages people to live fully in each moment, cultivate virtues like compassion and wisdom, and strive for spiritual growth, which may in turn influence death anxiety. This highlights the need for a study to collectively explore the reciprocal relationships of components that influence the fear of death and dying.
While much of the literature on the fear of death and dying focuses on psychological constructs, physical health is unquestionably a significant contributor to death anxiety. In fact, physical health might be the most conspicuous contributor to mortality salience due to its undeniable impact on psychological well-being and cognitive aging (Chalmers et al., 2022a). The COVID-19 pandemic posed a significant global challenge, with constant reminders of death in the media through daily updates and images (Menzies et al., 2020). During this time, the importance of immunity was dramatically highlighted by the surge in online sales of “immune boosters” (Menzies et al., 2020), underscoring that in times of existential threat from viruses, immunity becomes particularly salient as a coping mechanism. In addition, studies have found that high perceptions of immunity are associated with decreased death anxiety, and vice versa (Ermi̇ş, 2023).
Conversely, as death anxiety has been associated with psychological distress (White & Handal, 1991) and psychological distress, specifically anxiety, is associated with lower immunity (Breeze et al., 2024), it is likely that psychological distress mediates the relationship between death anxiety and immunity. Therefore, a study is needed to investigate the network of death anxiety facets and their positive or negative impacts on psychological and physical domains of well-being.
Only one study has conducted a network analysis of death anxiety, focusing on depression and anxiety in the elderly (Yang et al., 2022). No research has examined the interactive predictors of death anxiety (e.g., mindfulness, self-esteem, compassion) or its impact on well-being. The present study investigated associations among death anxiety, depression, anxiety, stress, mindfulness, compassion, immunity, spirituality, meaning in life, and self-esteem using Bayesian Gaussian graphical models (BGGM) and directed acyclic graph (DAG) network analysis. The aim was to elucidate the complex interactive effects of death anxiety and well-being variables by estimating their mutual relationships in a network model (Medvedev et al., 2021). Mapping these interactions reveals the impact of death anxiety impact on diverse well-being domains, and vice versa. A secondary aim was to identify differences in aspects of well-being between meditators and psychedelic users compared to the general population to guide potential intervention targets.
Method
Participants
A total of 572 participants were assessed for eligibility in the study, comprising 347 university students and 225 participants from the general population. Of those initially assessed, 37 participants did not provide consent and were excluded from the study. The remaining 535 participants were randomised; however, a further 49 participants were excluded from the final analysis due to either completing the survey in less than 5 min (n = 12) or completing less than 50% of the survey (n = 37), as these responses could potentially compromise the integrity and reliability of the results. This screening process resulted in a final sample of 486 participants who were selected for network analysis. An overview of the participant selection process is presented in Fig. 1.
Fig. 1
Flow chart of the study population
The demographic characteristics of the network analysis sample are summarised in Table 1. Participants ranged in age from 17 to 86 years (M = 27.11, SD = 12.98). The sample was predominantly female, comprising 356 females (73%) and 111 males (25%). A chi-square test confirmed that females significantly outnumbered males, χ2(1) = 395.50, p < 0.001.
Table 1
Demographics of the network analysis sample
Measure
Items
Frequency
Percentage
Gender
Male
111
23%
Female
356
73%
Unspecified
11
2%
Missing
8
2%
Ethnicity
NZ European
290
60%
Māori
86
18%
Asian
44
9%
Pasifika
15
3%
Other
43
9%
Missing
8
2%
Employment
Employed + Student
191
39%
Student
112
23%
Employed
107
22%
Unemployed + Student
25
5%
Unemployed Seeking Work
12
3%
Retired
15
3%
Employed + Student + Home Manger
10
2%
Other
15
3%
Relationship
Single
212
44%
De facto relationship
188
39%
Married
61
13%
Divorced
13
3%
Widowed
3
1%
Missing
9
2%
Religion
No Religion
265
55%
Christianity
114
24%
Māori Worldviews
30
6%
Hindu
8
2%
Buddhism
7
1%
Other
53
11%
Missing
9
1%
Life-Threatening Diseases
No Disease
435
90%
Cancer
6
1%
Diabetes
3
1%
Other
29
6%
Missing
13
2%
Meditation
Tried meditation at least once
346
71%
Never tried meditation
132
27%
Missing
8
2%
Psychedelic Experience
One or More Psychedelic Experiences
137
28%
Psychedelic Naïve
340
70%
Missing
9
2%
Most participants (69%) identified as students, with 191 combining employment with studies (39%) and 137 being solely students (28%). Among the remaining participants, 107 (22%) were employed. Regarding relationship status, the majority were single (212, 44%) or in a de facto relationship (188, 39%).
Religious affiliation data indicated that most participants did not follow any religion (265, 55%), while others identified as Christian (114, 24%) or adhered to other religions (46, 10%). A chi-square test showed significant differences in the distribution of religious affiliations, χ2(7) = 973.11, p < 0.001. Additionally, the majority of participants (435, 90%) reported no life-threatening diseases, which was also statistically significant, χ2(3) = 1937.16, p < 0.001.
The ethnic composition was diverse, with most participants identifying as New Zealand European (290, 60%), followed by Māori (86, 18%), Asian (44, 9%), Pacific Islander (15, 3%), and other ethnicities (43, 9%). Compared to the latest Statistics New Zealand data, the ethnic distribution of the sample appears broadly representative of the New Zealand population.
An adequate sample size for a network analysis depends on specifying the effect size for the network model, which differs from project to project depending on the focus of network centrality or recovering true edges (Constantin et al., 2023). However, Constantin (2018) identified that in a BGGM with less than 20 nodes, a sample size between 250 and 350 is adequate to detect true relationships with reliable quantification of strengths between the relationships of variables. As a rule of thumb, the greater the sample size, the greater the chance of a stable and accurate network (Hevey, 2018).
Procedure
Participants from the general population were acquired through various avenues. First, participants were recruited through advertisements on social media platforms, such as Facebook, and a snowball sampling method from connections of the primary researcher. The survey link was posted on various mental health pages, psychedelic user forums, and survey participation pages. Participants were asked to send the survey link to anyone who would be interested and willing to participate. An online invitation link was distributed via various social media platforms, which directed participants to the survey on Qualtrics. Second, the study was advertised on a local university website that was available to undergraduate psychology students. Students who participated in the study received an additional three credits towards their overall grade in their preferred psychology course. In addition, the study was sent to postgraduate clinical psychology students to complete if they were interested. University students were considered a part of the general population as there was no differentiation between full-time, part-time, and temporary international students. Therefore, this added complexity when differentiating a university student sample and the general population.
Measures
Sociodemographic Questionnaire
The following information was collected: gender, age, ethnicity, socioeconomic status, relationship status, employment status, experiences with life-threatening diseases, identification and engagement with religious beliefs, previous meditation experience, and previous psychedelic experiences.
Fear of Death Scale (FODS)
The self-report 32-item FODS (Lester & Abdel-Khalek, 2003) was developed to determine levels of four fears related to death: death of self, dying of self, death of others, and dying of others; all of which are 8-item subscales. For example, one question from the Death of Self subscale asks, “How disturbed or made anxious are you by the following aspects of your own death? The total isolation of death.” The FODS uses a 5-point Likert scale that ranges from Not anxious (1) to Very anxious (5) to determine the strength of fears related to death and dying in oneself and others. Item 8 in the Death of Others subscale, “Envious that the person is dead,” is the only item that needs to be reverse coded before computing the sum scores for each subscale. The FODS is a revised version of the 36-item Collett-Lester Fear of Death Scale (Lester, 1990). Internal consistency was computed for all four of the subscales using the current dataset. The internal consistency (Cronbach’s α) and reliability (McDonald’s ω) for the current dataset for four scales were Fear of Death (α = 0.89, ω = 0.89), Fear of Dying (α = 0.87, ω = 0.87), Fear of Others' Death (α = 0.83, ω = 0.83), and Fear of Others' Dying (α = 0.86, ω = 0.86).
Five Facet Mindfulness Questionnaire (FFMQ)
The FFMQ (Baer et al., 2006) is a 39-item rating scale that measures levels of these facets of mindfulness: observing internal and external experiences (Observe), describing experiences and sensations in words (Describe), acting with awareness (Act), nonreactivity to inner experiences (Nonreact), and nonjudgment to inner experiences (Nonjudge). For example, the Act subscale includes an item such as, “When I’m walking, I deliberately notice the sensations of my body moving”. These facets of mindfulness are divided into five subscales that are measured through a self-report 5-point Likert scale format that ranges from Never or rarely true (1) to Very often or always true (5). Nineteen negatively worded items are reverse coded before computing the sum scores of the subscales and total scale score. Higher scores depict greater levels of mindfulness. For the current dataset, Cronbach’s alpha (α = 0.90) and McDonald’s omega (ω = 0.89) suggest high levels of internal consistency and reliability, respectively.
Depression Anxiety and Stress Scales (DASS-21)
DASS-21 (Lovibond & Lovibond, 1995) is a 21-item self-report rating scale that has three subscales for assessing depression, anxiety, and stress (Cowles & Medvedev, 2022). One example item from the Stress subscale is, “I found it hard to wind down.” Each item is rated on a 4-point Likert scale ranging from Did not apply to me (0) to Applied to me very much, or most of the time. (4). There are no negatively worded items. Total subscale scores are computed by adding scores together within each scale. Subsequently, adding subscale scores together gives a total score for negative emotional states. Cronbach’s alpha (α) and McDonald’s omega (ω) for the current dataset were 0.94, indicating high levels of internal consistency and reliability, respectively.
Immunity Status Questionnaire (ISQ)
The ISQ (Wilod Versprille et al., 2019) is a 7-item self-report questionnaire for assessing the presence of physical illness in the last 12-month period. An example item is “sudden high fever.” The items are rated on a 5-point Likert scale ranging from Never (0) to (Almost) Always (4). The total raw score is the sum of all item responses. Raw scores were transformed using the algorithm developed by Wilod Versprille et al. (2019) as follows: if the raw score is ≥ 15 it is coded as 0, 14 = 1, 13 = 2, raw scores of 11 or 12 = 3, 10 = 4, raw scores of 8 or 9 = 5, 7 = 6, 6 = 7, 5 = 8, raw scores of 3 or 4 = 9, and raw scores ≤ 2 = 10, with higher scores contributing to better immunity. The cut-off for reduced immune functioning is a final score below 6 (ISQ < 6; Wilod Versprille et al., 2019). Internal consistency and reliability for the current dataset were Cronbach’s alpha (α) 0.66 and McDonald’s omega (ω) 0.65, respectively.
Santa Clara Brief Compassion Scale (SCBC)
The SCBC (Hwang et al., 2008) is a 5-item self-report instrument, determining one’s levels of compassion. One example item is, “When I hear about someone (a stranger) going through a difficult time, I feel a great deal of compassion for him or her.” The response metric ranges from “Not true of me at all” (1) to “Very true of me” (7). The total compassion score is the sum of ratings on all items. Internal consistency and reliability for the SCBC for the current dataset were Cronbach’s alpha (α = 0.89) and McDonald’s omega (ω = 0.88), respectively.
Royal Free Questionnaire for Spiritual & Religious Beliefs (RFQ)
Items 3, 7, 8, 9, 10, and 11 from the RFQ (King et al., 2001) were incorporated into the survey as these items were most relevant to the fear of death. For example, one item is, “Some people hold strongly to their views and others do not. How strongly do you hold to your religious/spiritual view of life?” These items explored the strength, importance, and helpfulness of religious beliefs in their personal lives, as well as the influence beliefs have in their lives. The items are rated on a 10-point Likert scale. The ratings for the 6 items were summed to provide a spirituality and religiosity score. Internal consistency and reliability for the current data were Cronbach’s alpha (α = 0.90) and McDonald’s omega (ω = 0.90), respectively.
Meaning in Life Questionnaire (MLQ)
The MLQ (Steger et al., 2006) is a 10-item self-report questionnaire that assesses an individual’s sense of meaning and purpose in life. One example item is, “I understand my life’s meaning.” The MLQ has two subscales: Presence of Meaning and Search for Meaning. Questionnaire items are rated on a 7-point Likert scale ranging from Absolutely untrue (1) to Absolutely true (10). Item 9 is the only negatively worded item in the scale that is reverse coded prior to summing the items for the subscales and total score. The higher the total scores, the greater meaning one has in their life. The internal consistency of the questionnaire was assessed using Cronbach's alpha and McDonald's omega. The Cronbach's alpha, calculated with all items, was 0.75. However, the calculation of McDonald's omega was impeded by Item 10, which had an item-total correlation of 0.137. Therefore, Item 10 was omitted in the McDonald's omega calculation, resulting in a McDonald's omega of 0.60. In the network analysis, Item 10 was retained as it is conceptually important item and removing this item may affect construct validity of the scale. We acknowledged low item-to-total correlation of this item as a limitation and recommended to address this issue in future psychometric studies in our Limitations and Future Directions section.
Rosenberg Self-Esteem Scale (RSES)
The RSES (Rosenberg, 1965) is a 10-item rating scale for assessing self-esteem. One example item is, “On the whole, I am satisfied with myself.” The RSES is scored on 4-point Likert scale ranging from Strongly agree (3) to Strongly disagree (0). Before calculating the total self-esteem score by summing the scores, Items 2, 5, 6, 8, and 9 need to be reverse coded. High levels of internal consistency and reliability were demonstrated in the current dataset, with both Cronbach’s alpha (α) and McDonald’s omega (ω) being 0.90, respectively.
Data Analyses
Descriptive statistics were computed in IBM SPSS v.29. Prior to formal analysis, there were 40 missing values in the total dataset after exclusion criteria (0.69% of the total dataset). A multiple imputation was chosen as precision is important even if the missing data percentage (0.69%) was negligible. The multiple imputation was conducted using the IterativeImputer from Scikit-Learn, which estimates the missing values iteratively (or bit-by-bit) based on the surrounding complete data to give the most accurate estimation of the missing values (Scikit-Learn Developers, 2023).
BGGM Network Estimation
To conduct the network analysis, R software (version 4.3.2) was applied, specifically using key packages, such as the “BGGM” package to estimate relationships between variables using Bayesian statistical methods (Williams & Mulder, 2020); “qgraph” to visualise the identified relationships between variables (Epskamp et al., 2012); and “networktools” to provide centrality and metrics for the networks (Jones, 2024).
A mixed-type BGGM with a prior standard deviation of 0.25 over 5000 bootstrap iterations was conducted as it allows us to robustly and reliably determine how variables influence each other (partial correlations networks) without long-run sample distributions to enable nonparametric data application (Williams, 2021). Bootstrapping is a resampling technique that draws multiple samples from the dataset repeatedly (an iteration) to create a sampling distribution that determines the confidence intervals (Chalmers et al. 2022b). Additionally, a combination of discrete and continuous data can be analysed in a mixed type BGGM (Hoff, 2007). Estimating partial correlations, excluding zero, across all variables was facilitated by employing a semi-parametric copula model based on ranked likelihood through the "explore" function, which utilised the "mixed" type (Williams, 2021). A non-zero partial correlation matrix implies that the population parameter will fall within that range at 95% of the time (Chalmers et al. 2022b). Significant associations were identified and included in the network graph.
The resulting network graph was generated using the Fruchterman-Reingold algorithm, depicting variables as nodes and significant partial correlations as edges (Fruchterman & Reingold, 1991). A thicker connection between two nodes (heavier edge weights) represents stronger relationships shown through a greater magnitude of the posterior mean. This indicates a higher degree of certainty of a strong partial correlation between two nodes. The edges between two nodes are significant partial correlations (associations) between two nodes. Nodes with no edges between them indicate that posterior means and CIs suggest there are no clear direct partial correlation when accounting for all other nodes. Node proximity also matched correlation strength to minimise overlap and enhance interpretability (Fruchterman & Reingold, 1991).
In our investigation focusing on the predictability of fear of death and dying through various other variables in the network, the emphasis lies primarily on understanding the extent to which this specific target node can be anticipated based on its connections. Predictability analysis, as described by Haslbeck and Waldorp (2018), effectively quantifies the variance in partial correlation network nodes that is accounted for by its neighbouring nodes, offering critical insights into the direct influences and dependencies within the network. In this context, the role of centrality, though valuable in broader network analyses, becomes less pertinent. Our primary interest is not in determining the overall influence or structural importance of each node within the network, as centrality measures would indicate (Haslbeck & Fried, 2017), but rather in pinpointing the specific predictive relationships that exist with respect to our target variable. By focusing on predictability, we can more directly target those variables that have the most significant impact on the fear of death and dying, thereby providing a more streamlined and relevant understanding that is crucial for developing targeted interventions. This approach aligns with the practical goals of our study, making the additional analysis of centrality indices less relevant to our specific objectives.
Directed Acyclic Graph (DAG) Analysis
The “bnlearn” package in R was used to learn the structure of a Bayesian network from the data and determine the direction of relationships between variables (Chalmers et al. 2022b). This process is reflected in a directed acyclic graph (DAG). The hill-climbing algorithm was applied, which adds, removes, or reverses edges in the network to optimise the Bayesian Information Criterion (BIC) score. This process builds a network that best fits the relationships in the data. To improve stability, bootstrapping with 5000 samples was used to create multiple sampled networks (Blanchard et al., 2021). Edges were retained that appeared in at least 51% of these bootstrap network samples. The averaged network across all bootstrap samples was then plotted and edge strength computed based on the BIC score. Bootstrapping and averaging networks address potential issues with sampling variability. The final averaged DAG network illustrates the most stable and likely connections in the data. The thickness of arrows in the visual representation indicates either the impact on model fit from removing that edge (based on BIC) or the likelihood that the edge exists in that direction (based on bootstrap samples).
Independent Samples t-Tests
As a secondary analysis, independent samples t-tests were conducted between psychedelic users (n = 137) and non-users, as well as meditators (n = 115) and non-meditators to determine any significant differences in scale scores. Psychedelic users were characterised by having at least one psychedelic experience, whereas meditators were characterised by having meditated at least more than once (semi-regularly). An alpha level of 0.05 was used as the threshold for statistical significance in these exploratory analyses. These analyses were computed in IBM SPSS v.29.
Results
Means and standard deviations of all variables included in the network, such as distress, protective, health, and demographic factors, are displayed in Supplementary Table S1. Figure 2 displays the estimated network (BGGM) of variables associated with the fear of death and dying about oneself and others. As expected, there are strong positive relationships between fear of others’ death and fear of others’ dying, as well as fear of death and fear of dying. This implies that if one has a strong fear of death, it is expected that they will also have a strong fear of dying.
Fig. 2
Network analysis of variables associated with the fear of death and dying about oneself and others. Note. BGGM network only displays significant links with credible intervals (95% confidence intervals) that do not include zero. Red edges (lines) denote negative relationships; blue edges (lines) represent positive relationships; the thickness and boldness of the colour illustrate stronger relationships (Borsboom, 2017)
There was a negative relationship between fear of death and both mindfulness and distress. Conversely, fear of dying was positively related to both mindfulness and distress. Fear of others' dying was solely associated with elevated spirituality; however, this association was weak, as displayed in Supplementary Table S2. Fear of others' death had the strongest correlations with other variables, including lower spirituality, greater meaning in life, weakened physical immunity, and diminished mindfulness. Notably, self-esteem levels showed no significant association with any facets of death anxiety, while levels of meaning in life only correlated with heightened fear of others' dying.
A predictability analysis was conducted to determine which variables were the most strongly predicted by other variables in the network. Figure 3 shows that fear of dying has the highest predictability: This means that among all the factors studied, how much a person fears dying can be most accurately predicted by other related factors in the network. Fear of others' dying, fear of death, and fear of others' death follow: Similar to fear of dying, these related fears are also highly predictable based on the network. They likely share common predictors or are influenced heavily by similar factors. Self-esteem is also highly predictable: After the fears related to death, self-esteem is the next factor that can be well-predicted by other variables in the network. This might suggest that self-esteem is closely linked to how a person feels about death and other factors in the network. Compassion, spirituality, and socioeconomic status have lower predictability: These variables are less predictable by others in the network, meaning they might be influenced by a broader array of factors or are less directly connected to the central themes of the network compared to death and self-esteem.
Fig. 3
Predictability analysis of variables in death anxiety network. Note. Predictability measured in Bayes R.2
Directed Acyclic Graph (DAG) Analysis
While the BGGM offers an insight into which variables are significantly related, the DAG expands on this to denote the direction of these relationships to discern the way these variables directly affect each other. Figure 4 illustrates the DAG, which denotes the directions of relationships (shown by arrows) and potential causation through applying Bayesian statistical theory to estimate probabilistic dependencies (Chalmers et al., 2022a, 2022b). The thickness between these arrows depicts the strength and directionality of the relationship learned through the bootstrapping procedure. For example, the fear of others’ death is in the highest position on the DAG and predicted the onset of other variables, including the fear of others’ dying, the fear of death, the fear of dying, mindfulness, and meaningfulness in life. A bootnet analysis revealed that the fear of others’ death was strongly connected to the fear of others’ dying, with an edge strength of 1.0 and a directionality percentage of 81%. This implies that throughout the bootstrapping process of 5000 iterations an edge strength of 1.0 occurred 81% of the time, depicting a robust predictive relationship. The fear of others’ death predicted one’s level of mindfulness (0.88 strength in 59%) – greater fear of others’ death predicts lower levels of mindfulness. Additionally, mindfulness strongly predicted the variance in self-esteem (1.0 strength in 62%) and distress (0.99 strength in 49%) – greater levels of mindfulness predicted higher self-esteem and lower psychological distress.
Fig. 4
A directed acyclic graph (DAG) of the Bayesian network of variables associated with death anxiety. Note. A thicker arrow signifies a higher percentage of occurrences in the bootstrapped networks where the arrow is oriented in that specific direction, vice versa. In simpler terms, when the arrow is thicker, it means that more often in our analysed networks, events tend to go in that direction. The thickness indicates the frequency of that particular direction compared to the opposite. Node definitions: odeath = fear of others’ death; dying = fear of dying; death = fear of death; odying = fear of others’ dying; mindful = mindfulness; sest = self-esteem; ses = socioeconomic status; distress = psychological distress; meaning = meaning in life; spiritual = spirituality & religiosity; isq = self-reported immunity
Self-esteem predicted the variance in socioeconomic status (1.0 strength in 92%), distress (1.0 strength in 40%), and meaningfulness in life (1.0 strength in 91%). In other words, self-esteem predicts lower psychological distress levels whilst also greater socioeconomic status and meaningfulness in life. Compassion did not appear to be influenced by any other variables including any of the death anxiety facets; however, compassion appeared to have a strong probability of being influenced by psychological distress (0.46 strength in 61%), meaningfulness in life (0.99 strength in 56%), and spirituality/religiosity (0.99 strength in 49%). Thereby, compassion predicts greater levels of meaningfulness in life and spirituality/religiosity, and lower psychological distress.
Levels of immunity were predicted through the fear of others’ death (0.97 strength in 64%) and psychological distress (1.0 strength in 63%). Consequently, this implies that greater psychological distress levels and a greater fear of others’ deaths negatively impacts one’s immunity. Furthermore, spirituality/religiosity was predicted through compassion (0.99 strength in 49%) and meaningfulness in life (0.93 strength in 55%). Therefore, this implies that greater compassion and meaningfulness in life is associated with higher levels of spirituality.
Psychedelic and Meditation Predictors
To determine whether psychedelic use and meditation experience was associated with differences in scale scores, independent t-tests were conducted between psychedelic users and non-users, as well as meditators and non-meditators. In the current sample, psychedelic users had significantly lower fears of death, fears of dying, fears of others’ death, and fears of others’ dying. In addition, psychedelic users had significantly greater levels of mindfulness. Upon calculation of effect sizes, fear of death and fear of others’ dying emerged with large effect sizes. Meanwhile, fear of others’ death and mindfulness exhibited moderate effect sizes, with fear of dying displaying a relatively smaller effect size. Supplementary Table S3 displays the means of each group, t-test results, and effect sizes of all statistically significant variables.
Meditators had significantly greater spirituality, compassion, meaning in life, self-esteem, and mindfulness than non-meditators. Furthermore, meditators had significantly lower fears of others’ death, fears of others’ dying, psychological distress. After computing effect sizes, no large effect sizes were identified. However, mindfulness and spirituality elicited moderate effect sizes, whereas the majority demonstrated small effect sizes: fear of others’ dying, fear of others’ death, psychological distress, self-esteem, meaning in life, and compassion. Supplementary Table S4 displays the means of each group, t-test results, and effect sizes of all statistically significant variables.
Discussion
The aim of the present study was to identify unique associations and reciprocal influences between death anxiety and well-being related variables, which could manifest in various impacts on life. The results show that the fear of another's death was associated with greater meaning in life and enhanced spirituality. While death anxiety is commonly regarded as a pathological health concern in psychological literature due to its contribution to psychological distress (Iverach et al., 2014; Semenova & Stadtlander, 2016; Upenieks, 2023), our findings reveal a more nuanced relationship. Specifically, the present study found that fear of dying showed a significant positive association with psychological distress, while fear of death demonstrated a negative association with distress. These results suggest that different aspects of death anxiety may have distinct relationships with psychological distress, which is a novel contribution of this study. In fact, fear of death and dying, both towards oneself and of others, was not associated with well-being. These results expand on TMT by adding novel empirical evidence that emphasise the importance of recognising mortality to acquire greater meaning in life.
As a secondary analysis, differences among well-being variables were examined between meditators and non-meditators, as well as between psychedelic users and non-users, to identify areas that could potentially reduce the fear of death and dying. Psychedelic users had significantly lower death anxiety on all facets of the FODS. In addition, meditators had significantly lower overall distress, fears of others’ death, and fears of others’ dying. The present study illustrates the network of variables associated with death anxiety without missing relationships that may be mutually maintaining or reciprocal.
The BGGM network analysis revealed several significant non-directional associations between the variables of interest. Notably, the fear of another's death emerged as a central node, exhibiting positive correlations with higher meaning in life, lower spirituality, increased life limiting illnesses, and diminished mindfulness. Conversely, the fear of dying correlated with higher mindfulness and distress, while the fear of others' dying was weakly associated solely with elevated spirituality. Interestingly, self-esteem showed no significant associations with any facets of death anxiety. While the BGGM elucidates these unique relationships, its non-directional nature limits the interpretation of causality or the directional flow of influence among the variables. In addition, as a correlational approach, the BGGM cannot conclusively establish causal relationships, highlighting the need for complementary analytical techniques to uncover potential directional pathways.
To address the limitations of the BGGM and elucidate potential causal pathways, a directed acyclic graph (DAG) was constructed using Bayesian network analysis. The DAG showed that the fear of another's death occupied a central position, exhibiting a robust potential to influence other variables, including fear of death, fear of dying, fear of others' dying, mindfulness, and meaning in life. Specifically, fear of another's death demonstrated a strong likelihood of decreasing mindfulness and negatively impacting life limiting illness, while increasing meaning in life, which in turn predicted greater spirituality.
Conversely, self-esteem did not exhibit a significant influence on facets of death anxiety, which contradicts the traditional TMT perspective (Greenberg et al., 1986). While recent research continues to support the role of self-esteem as a buffer for death anxiety (Abeyta et al., 2014; Routledge et al., 2010), our findings suggest this relationship may be more nuanced or context dependent. This result is inconsistent with much of the TMT literature, which typically finds self-esteem to buffer against the negative effects of death anxiety, particularly in Western populations (Du et al., 2013). However, it aligns with studies showing that, in some cases, self-esteem may not have as strong an effect in reducing death anxiety, especially in non-clinical groups (Routledge, 2012). These results suggest the need for further investigation into when and why self-esteem fails to buffer death anxiety. In this study, self-esteem predicted lower psychological distress, higher socioeconomic status, and greater meaning in life. The DAG analysis provides a directional perspective, suggesting potential causal pathways wherein fear of another's death may initiate a cascade of effects on various well-being domains. These findings need to be substantiated in longitudinal and intervention studies.
The predictability results suggest that fears related to death are closely interlinked and can influence one another. The extent of someone’s fear of death and related aspects can be somewhat anticipated by considering other personal fears and relevant psychological factors. Self-esteem's connection to these fears may indicate that how one views oneself is significantly impacted by one's attitudes towards death and other factors in the network, such as compassion, spirituality, and socioeconomic status. Overall, these results validate the relevance of predictors included in the network based on theoretical and empirical research findings (Greenberg et al., 1986). Variables such as compassion, spirituality, and socioeconomic status may be influenced by a wider variety of factors and are not as directly predictable from the network of death-related fears or self-esteem.
Little research has focused on the reciprocal relationships between death anxiety and various well-being variables in the general population. Despite the unfavourable connotations associated with death anxiety, the present results primarily suggest the contrary. According to the DAG analysis, the fear of another’s death significantly predicted outcomes for fear of death and dying, and the fear of others’ dying. However, these three facets did not significantly predict variations in any aspect of well-being. Essentially, the present study found that only the fear of another’s death had a robust potential to affect various facets of well-being. None of the well-being variables exhibited a significant likelihood of exerting influence on death anxiety; in other words, it does not appear that psychological distress, mindfulness, compassion, life limiting illness, spirituality, meaning in life, or self-esteem would affect death anxiety. Similarly, death anxiety does not have any significant detrimental associations with well-being, except lower mindfulness and more frequent life limiting illnesses.
Meaning in life has been suggested to directly alleviate death anxiety (Juhl & Routledge, 2016). TMT also suggests that cultivating sufficient self-esteem and connecting with individuals who share similar cultural worldviews will enhance meaning in life and buffer the effects of death anxiety (Greenberg et al., 1986). However, the present results do not align with these claims. The fear of another’s death was shown to have a strong likelihood of increasing meaning in life, which would likely improve spirituality and religiosity. In this sense, the present results align more closely with the MMT, emphasising that experiencing and accepting the idea of impermanence can lead to greater meaning in life and more spirituality (Neimeyer et al., 2002; Wong, 2007; Zhang et al., 2019). Therefore, in the general population it is questionable whether the fear of death and dying should be viewed as a negative phenomenon, as it can potentially improve meaning in life and spirituality, both of which are associated with greater self-esteem and compassion.
The fear of one's own death has a small likelihood of directly impacting any aspect of well-being. In contrast, the fear of another's death does, highlighting the potential significance of social relationships for human well-being. Positive social relationships, such as family, romantic partners, and friends, have been shown to precipitate greater meaning in life (O’Donnell et al., 2014). This may also explain why one fears the death of others because one would lose a sense of meaning in life. In addition, compassion has previously been linked to improved social connections, which may explain why compassion has a substantial aptitude to more meaning in life (Seppala et al., 2013; Wheeler, 2001). Moreover, compassion accounted for variations in psychological distress, indicating that higher levels of compassion correspond to lower levels of psychological distress, as suggested by Wheeler (2001). Consequently, a central theme arises underscoring the significance of social connections and concern for others in fostering a fulfilled life with diminished psychological distress, aligning closely with the principles of the TMT.
Mindfulness did not significantly reduce death anxiety, which opposes the therapeutic findings from current death anxiety mindfulness interventions (Askarizadeh et al., 2022; Xiang-Zi & Jia-yuan, 2023). In addition, mindfulness did not significantly influence compassion or meaning in life in any significant manner, contradicting the findings of Lim et al. (2015) and Tan et al. (2023). No mediating effects were found from mindfulness that would likely lower death anxiety (Baharvandi et al., 2020; Dursun et al., 2022).
Mindfulness appears to contribute to lower levels of psychological distress, as reported in earlier studies (Medvedev et al., 2021; Roemer et al., 2021). Mindfulness was positively associated with self-esteem, with higher levels of mindfulness predicting higher levels of self-esteem. While self-esteem seems to contribute to greater meaning in life and lower psychological distress, aligning with TMT, the present findings contradict previous research by failing to demonstrate an association between self-esteem and lower death anxiety (Greenberg et al., 1986; Routledge, 2012; Wisman & Heflick, 2016). Though prior studies indicate that self-esteem acts as a buffer against death anxiety in support of TMT (Greenberg et al., 1986), the present results contradict this established link. The lack of this relationship challenges a core TMT tenet that self-esteem functions to manage death anxiety. Consequently, these findings imply that self-esteem may not serve an anxiety-buffering purpose in the general population as proposed by TMT (Greenberg et al., 1986). While self-esteem may still influence meaning in life and distress through other mechanisms, the failure to replicate the previously observed anxiety-buffering effect of self-esteem undermines a key postulate of TMT regarding the role of self-esteem.
An interesting implication arises between the fear of one’s own death and the fear of another’s death, and their consequent impact on well-being. As fear of another’s death is associated with greater fear of one’s own death, dying, and others’ dying in the network model, it appears that fear of another's death could be the core of death anxiety itself. It is logical that as individuals encounter the grief and trauma of losing loved ones over the course of their lives, this experience could intensify and strengthen their fear of their own mortality and dying process. This interpretation aligns with foundational Buddhist teachings, which emphasises how witnessing illness, aging, and death among others sparked Siddhartha's own confrontation with impermanence and fears around his mortality, catalysing his pursuit of awakening. This illustrates the potential for the experience of others' mortality to initiate an awakening of one's own death and spark existential growth. The network analysis in the present study quantitatively maps these qualitative insights from Buddhist philosophy and prior research, demonstrating the potential centrality of the fear of another’s death in shaping broader death anxiety through its impact on related fears about one’s own and loved ones’ mortality.
Although this study offers valuable insights into the relationships between death anxiety and well-being variables, it was limited by the lack of cultural diversity within the sample. While sociodemographic factors such as age and cultural background were considered, the predominant New Zealand-based sample did not provide sufficient cultural variation to accurately assess their impact. These factors have been previously found to influence death anxiety and its related constructs (Kashima et al., 2004; Soleimani et al., 2017). The exclusion of sociodemographic variables may limit the generalisability of the findings, as they could contribute to variations in death anxiety and well-being across different populations. Future research should consider including sociodemographic predictors to gain a more nuanced understanding of how these factors shape experiences of death anxiety and its impact on well-being.
Limitations and Future Directions
This study has several limitations that should be considered when interpreting the results. Given that the sample consisted of university students and community participants, it is challenging to treat the total sample as representative of the general population. It is well known that the meaning of mindfulness and its practice in students can differ from those in community-based participants. Furthermore, the sample was skewed with a predominance of New Zealand ethnicities, which limits the generalisability of the findings to other ethnic groups and cultural contexts. Death anxiety and its related factors may vary significantly across different ages and cultures, and these cultural differences could influence the results (Kashima et al., 2004; Maxfield et al., 2007). For example, collectivist societies like Japan have been shown focus on collective mortality, whereas individualistic cultures like Australia emphasise personal mortality (Kashima et al., 2004), and older adults may experience heightened anxiety due to increased mortality salience (Maxfield et al., 2007). Additionally, this study's cross-sectional design limits our ability to draw causal conclusions about the relationship between death anxiety and well-being factors. Death anxiety can change based on a person's circumstances and environment, which a single-point assessment does not fully capture. For example, Future studies that follow participants over time could provide deeper insights into how death anxiety and related factors change. This approach would offer a more complete picture of the relationships we have observed in our current research, helping us better understand how these variables interact over time.
To strengthen future research in this area, it would be beneficial to include more diverse samples in terms of ethnicity and cultural background. This would provide a broader understanding of how death anxiety and its related factors are experienced across different populations. Additionally, investigating the role of clinical status in death anxiety could offer insights into how these factors interact in clinical versus non-clinical populations. Future studies might also address the limitations of small sample sizes in secondary analyses by including larger and more varied groups, such as those practising different forms of meditation or using psychedelic substances.
One limitation of this study is the lack of consideration for the role of trauma in moderating the relationship between death anxiety and positive outcomes, such as meaning in life or spiritual growth. Traumatic experiences, especially those occurring early in life, may make it harder for people to deal with their fear of death in a healthy way. This difficulty might prevent them from finding meaning or experiencing personal growth when confronting death-related thoughts (Kesebir et al., 2011). On the other hand, post-traumatic growth can sometimes occur when people face death-related fears, particularly in situations like having a life-threatening illness such as cancer (Vail et al., 2012). Future studies should look into how trauma, fear of death, and post-traumatic growth influence each other. This research could help us better understand the complex relationships between these factors.
One of the limitations of our study is the inclusion of an item in the MLQ (Steger et al., 2006) with a low item-to-total correlation (Item 10). This low correlation (0.137) indicates that Item 10 does not align well with the overall construct measured by the questionnaire. While this did not substantially affect the overall reliability as measured by Cronbach's alpha (0.75), it may impact omega calculations which consider factor loadings—a matter of construct validity rather than reliability. Although Item 10 was retained in the network analysis due to its conceptual importance, its poor performance in relation to other items suggests potential construct validity concerns. Future studies should address construct validity by examining and potentially revising Item 10 in the MLQ to better correlate with other items while maintaining its conceptual significance. Additionally, conducting longitudinal studies could help assess how changes in the MLQ's items and overall construct might impact long-term outcomes and interactions between death anxiety and well-being. Controlled trials could also be valuable in testing specific interventions designed to address the issues identified with MLQ items and evaluating their effectiveness in improving measurement reliability. These approaches would enhance our understanding of the MLQ's effectiveness and provide a more robust foundation for future research in this area.
Another limitation in our study is the inclusion of RFQ items that assumed participants held religious or spiritual beliefs. Specifically, participants in the original questionnaire who indicated no religious or spiritual affiliation were instructed to skip the items we included in our analysis, which may have led to incongruence for those individuals. With 265 participants reporting no religion, the inclusion of these items may not fully capture their experiences or beliefs. Additionally, religiosity and spirituality were summed and analysed together, even though these are distinct constructs. For example, an individual may subscribe to high levels of spirituality and engage in meditative practices but not be religious. This conflation could have introduced variability in how participants' beliefs and practices were measured. Future research should address this limitation by selecting items that better differentiate between religiosity and spirituality, as well as those applicable to individuals with a range of belief systems. Incorporating additional measures that account for varying levels of religiosity and spirituality would help ensure a more comprehensive assessment and better reflect the diversity of participants' beliefs.
Differences in death anxiety and well-being were investigated among psychedelic users and non-users, as well as meditators and non-meditators, to assess their potential as interventions for death anxiety. Although there was suggestive evidence of the positive effects of psychedelics on death anxiety, the small sample size precluded firm conclusions about their effects. While meditators had significantly lower overall distress, fears of others’ death, and fears of others’ dying than non-meditators, it is difficult to parse the direct effects on death anxiety. The difficulty arises from the lack of information on the nature, history, and duration of daily meditation practices by the participants. Therefore, no intervention options should be inferred from this study.
The present study raises questions that can be explored in future research. For example, current conceptualisations of death anxiety as universally pathological could be investigated further, given the finding of positive effects of death anxiety in this study. While TMT posits death anxiety as detrimental, the present results suggest that the fear of another’s death is associated with greater meaning in life and spirituality. This aligns more with perspectives like MMT, which views embracing mortality as an impetus for positive changes in meaning in life (Wong, 2007).
The network analysis approach reveals predictive relations between variables that can inform targeted interventions. For instance, while self-esteem and mindfulness displayed no significant direct effects on death anxiety, strengthening mindfulness, self-esteem, and compassion could be beneficial targets given their potential impact on meaning in life, psychological distress, and spirituality. Whether the predictive relations derived through network analyses can result in positive intervention outcomes remains to be demonstrated.
Acknowledgements
The authors would like to sincerely thank the participants of this study for generously sharing their time and experiences. We are also grateful to the reviewers for their thoughtful feedback, which significantly contributed to the improvement of this article.
Declarations
Ethical Approval
This study was conducted in compliance with the guidelines of the University of Waikato of the ALPSS Ethics Committee.
Informed Consent
All participants provided informed consent prior to beginning the questionnaire, having been presented with information about the purpose of the study, their rights as participants, and the voluntary and anonymous nature of their participation.
Conflicting interests
The authors declare no conflict of interest.
Use of Artificial Intelligence
This research did not use AI tools.
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