Top

Gepubliceerd in:

26-06-2023

Development and initial psychometric evaluation of a COVID-related psychosocial experiences questionnaire for cancer survivors

Auteurs: Estefany Saez-Clarke, Amy K. Otto, Sarah Prinsloo, Akina Natori, Richard W. Wagner, Telma I. Gomez, Jewel M. Ochoa, Shelley S. Tworoger, Cornelia M. Ulrich, Cassandra A. Hathaway, Anita R. Peoples, Michael H. Antoni, Julienne E. Bower, Lorenzo Cohen, Frank J. Penedo

Gepubliceerd in: Quality of Life Research | Uitgave 12/2023

Abstract

Background

Cancer survivors are at elevated risk of psychological problems related to COVID-19, yet no published measure adequately assesses their psychosocial experiences during the pandemic.

Purpose

Describe the development and factor structure of a comprehensive, self-report measure (COVID-19 Practical and Psychosocial Experiences questionnaire [COVID-PPE]) assessing the pandemic’s impact on US cancer survivors.

Methods

The sample (n = 10,584) was divided into three groups to assess COVID-PPE factor structure by conducting: (1) initial calibration/exploratory analysis of the factor structure of 37 items (n = 5070), (2) confirmatory factor analysis of the best-fitting model (36 items after item removal; n = 5140), and (3) post-hoc confirmatory analysis with an additional six items not collected in the first two groups (42 items; n = 374).

Results

The final COVID-PPE was divided into two sets of subscales, conceptualized as Risk Factors and Protective Factors. The five Risk Factors subscales were labeled Anxiety Symptoms, Depression Symptoms, Health Care Disruptions, Disruptions to Daily Activities and Social Interactions, and Financial Hardship. The four Protective Factors subscales were labeled Perceived Benefits, Provider Satisfaction, Perceived Stress Management Skills, and Social Support. Internal consistency was acceptable for seven subscales (αs = 0.726–0.895; ωs = 0.802–0.895) but poor or questionable for the remaining two subscales (αs = 0.599–0.681; ωs = 0.586–0.692).

Conclusions

To our knowledge, this is the first published self-report measure comprehensively capturing psychosocial impact—both positive and negative—of the pandemic on cancer survivors. Future work should evaluate predictive utility of COVID-PPE subscales, particularly as the pandemic evolves, which may inform recommendations for cancer survivors and facilitate identification of survivors most in need of intervention.

vorige artikel Health outcomes before and during the COVID-19 pandemic in caregivers of service members and veterans with traumatic brain injury

volgende artikel Comparative effectiveness of verbal instruction versus video-based education (VIVid) among family caregivers for improving the quality of life in advanced head and neck cancer patients receiving palliative care in Eastern India: a randomized controlled trial

Alleen toegankelijk voor geautoriseerde gebruikers

Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M., & Agha, R. (2020). The socio-economic implications of the coronavirus pandemic (COVID-19): A review. International Journal of Surgery, 78, 185–193. https://doi.org/10.1016/j.ijsu.2020.04.018CrossRefPubMed

McGinty, E. E., Presskreischer, R., Han, H., & Barry, C. L. (2020). Psychological distress and loneliness reported by us adults in 2018 and April 2020. JAMA, 324(1), 93–94. https://doi.org/10.1001/jama.2020.9740CrossRefPubMedPubMedCentral

Bureau, U. S. C. (2021). Household pulse survey: Measuring social and economic impacts during the coronavirus pandemic. https://www.census.gov/programs-surveys/household-pulse-survey.html

Hansel, T. C., Saltzman, L. Y., Melton, P. A., Clark, T. L., & Bordnick, P. S. (2022). COVID-19 behavioral health and quality of life. Scientific Reports, 12(1), 961. https://doi.org/10.1038/s41598-022-05042-zCrossRefPubMedPubMedCentral

Liang, W., Guan, W., Chen, R., Wang, W., Li, J., Xu, K., Li, C., Ai, Q., Lu, W., Liang, H., Li, S., & He, J. (2020). Cancer patients in sars-cov-2 infection: A nationwide analysis in China. Lancet Oncology, 21(3), 335–337. https://doi.org/10.1016/S1470-2045(20)30096-6CrossRefPubMed

Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., Zhao, Y., Li, Y., Wang, X., & Peng, Z. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan. China. JAMA, 323(11), 1061–1069. https://doi.org/10.1001/jama.2020.1585CrossRefPubMed

Onder, G., Rezza, G., & Brusaferro, S. (2020). Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA, 323(18), 1775–1776. https://doi.org/10.1001/jama.2020.4683CrossRefPubMed

Bakouny, Z., Hawley, J. E., Choueiri, T. K., Peters, S., Rini, B. I., Warner, J. L., & Painter, C. A. (2020). COVID-19 and cancer: Current challenges and perspectives. Cancer Cell, 38(5), 629–646. https://doi.org/10.1016/j.ccell.2020.09.018CrossRefPubMedPubMedCentral

Williams, P. A., Zaidi, S. K., & Sengupta, R. (2022). AACR Report on the Impact of COVID-19 on cancer research and patient care. Clinical Cancer Research, 28(4), 609–610. https://doi.org/10.1158/1078-0432.CCR-22-0192CrossRefPubMed

10.

Aziz, N. M. (2007). Cancer survivorship research: State of knowledge, challenges and opportunities. Acta Oncologica, 46(4), 417–432. https://doi.org/10.1080/02841860701367878CrossRefPubMed

11.

Reed, S. C., Bell, J. F., Miglioretti, D. L., Nekhlyudov, L., Fairman, N., & Joseph, J. G. (2020). Fear of cancer recurrence and associations with mental health status and individual characteristics among cancer survivors: Findings from a nationally representative sample. Journal of Psychosocial Oncology, 38(2), 125–142. https://doi.org/10.1080/07347332.2019.1649338CrossRefPubMed

12.

Aaronson, N. K., Mattioli, V., Minton, O., Weis, J., Johansen, C., Dalton, S. O., Verdonck-de Leeuw, I. M., Stein, K. D., Alfano, C. M., Mehnert, A., de Boer, A., & van de Poll-Franse, L. V. (2014). Beyond treatment - psychosocial and behavioural issues in cancer survivorship research and practice. European Journal of Cancer Supplements, 12(1), 54–64. https://doi.org/10.1016/j.ejcsup.2014.03.005CrossRefPubMedPubMedCentral

13.

Carrera, P. M., Kantarjian, H. M., & Blinder, V. S. (2018). The financial burden and distress of patients with cancer: Understanding and stepping-up action on the financial toxicity of cancer treatment. CA: A Cancer Journal for Clinicians, 68(2), 153–165. https://doi.org/10.3322/caac.21443CrossRefPubMed

14.

Alfano, C. M., & Rowland, J. H. (2006). Recovery issues in cancer survivorship: A new challenge for supportive care. The Cancer Journal, 12(5), 432–443. https://doi.org/10.1097/00130404-200609000-00012CrossRefPubMed

15.

Chan, A., Ashbury, F., Fitch, M. I., Koczwara, B., Chan, R. J., & Group, M. S. S. (2020). Cancer survivorship care during COVID-19-perspectives and recommendations from the MASCC survivorship study group. Supportive Care in Cancer, 28(8), 3485–3488. https://doi.org/10.1007/s00520-020-05544-4CrossRefPubMedPubMedCentral

16.

Kutikov, A., Weinberg, D. S., Edelman, M. J., Horwitz, E. M., Uzzo, R. G., & Fisher, R. I. (2020). A war on two fronts: Cancer care in the time of COVID-19. Annals of Internal Medicine, 172(11), 756–758. https://doi.org/10.7326/M20-1133CrossRefPubMed

17.

Mazidimoradi, A., Tiznobaik, A., & Salehiniya, H. (2022). Impact of the COVID-19 pandemic on colorectal cancer screening: A systematic review. Journal of Gastrointestinal Cancer, 53, 730–744. https://doi.org/10.1007/s12029-021-00679-xCrossRefPubMed

18.

Tope, P., Farah, E., Ali, R., El-Zein, M., Miller, W. H., & Franco, E. L. (2023). The impact of lag time to cancer diagnosis and treatment on clinical outcomes prior to the COVID-19 pandemic: A scoping review of systematic reviews and meta-analyses. elife, 12, e81354. https://doi.org/10.7554/eLife.81354CrossRefPubMedPubMedCentral

19.

Jabbal, I. S., Sabbagh, S., Dominguez, B., Itani, M., Mohanna, M., Samuel, T., & Nahleh, Z. (2023). Impact of COVID-19 on cancer-related care in the United States: An overview. Current Oncology, 30, 681–687. https://doi.org/10.3390/curroncol30010053CrossRefPubMedPubMedCentral

20.

Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The fear of COVID-19 scale: Development and initial validation. International Journal of Mental Health and Addiction, 20, 1537–1545. https://doi.org/10.1007/s11469-020-00270-8CrossRefPubMedPubMedCentral

21.

Arpaci, I., Karatas, K., & Baloglu, M. (2020). The development and initial tests for the psychometric properties of the COVID-19 phobia scale (C19P-S). Personality and Individual Differences, 164, 110108. https://doi.org/10.1016/j.paid.2020.110108CrossRefPubMedPubMedCentral

22.

Lee, S. A. (2020). How much “thinking” about COVID-19 is clinically dysfunctional? Brain, Behavior, and Immunity, 87, 97–98. https://doi.org/10.1016/j.bbi.2020.04.067CrossRefPubMedPubMedCentral

23.

Lee, S. A. (2020). Coronavirus anxiety scale: A brief mental health screener for COVID-19 related anxiety. Death Studies, 44(7), 393–401. https://doi.org/10.1080/07481187.2020.1748481CrossRefPubMed

24.

Qiu, J., Shen, B., Zhao, M., Wang, Z., Xie, B., & Xu, Y. (2020). A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations. General Psychiatry. https://doi.org/10.1136/gpsych-2020-100213CrossRefPubMedPubMedCentral

25.

Forte, G., Favieri, F., Tambelli, R., & Casagrande, M. (2020). COVID-19 pandemic in the Italian population: Validation of a post-traumatic stress disorder questionnaire and prevalence of PTSD symptomatology. International Journal of Environmental Research and Public Health, 17(11), 4151. https://doi.org/10.3390/ijerph17114151CrossRefPubMedPubMedCentral

26.

McElroy, E., Patalay, P., Moltrecht, B., Shevlin, M., Shum, A., Creswell, C., & Waite, P. (2020). Demographic and health factors associated with pandemic anxiety in the context of COVID-19. British Journal of Health Psychology, 25(4), 934–944. https://doi.org/10.1111/bjhp.12470CrossRefPubMed

27.

Zenker, S., Braun, E., & Gyimóthy, S. (2021). Too afraid to travel? Development of a pandemic (COVID-19) anxiety travel scale (PATS). Tourism Management, 84, 104286. https://doi.org/10.1016/j.tourman.2021.104286CrossRefPubMedPubMedCentral

28.

Mayer, Y., Etgar, S., Shiffman, N., & Lurie, I. (2021). The fear of COVID-19 familial infection scale: Development and initial psychometric examination. Measurement and Evaluation in Counseling and Development, 55(2), 71–83. https://doi.org/10.1080/07481756.2021.1998780CrossRef

29.

Behar-Zusman, V., Chavez, J. V., & Gattamorta, K. (2020). Developing a measure of the impact of COVID-19 social distancing on household conflict and cohesion. Family Process, 59(3), 1045–1059. https://doi.org/10.1111/famp.12579CrossRefPubMedPubMedCentral

30.

Yıldırım, M., & Solmaz, F. (2022). COVID-19 burnout, COVID-19 stress and resilience: Initial psychometric properties of COVID-19 burnout scale. Death Studies, 46(3), 524–532. https://doi.org/10.1080/07481187.2020.1818885CrossRefPubMed

31.

Taylor, S., Landry, C. A., Paluszek, M. M., Fergus, T. A., McKay, D., & Asmundson, G. J. G. (2020). Development and initial validation of the COVID stress scales. Journal of Anxiety Disorders, 72, 102232. https://doi.org/10.1016/j.janxdis.2020.102232CrossRefPubMedPubMedCentral

32.

Feng, L.-S., Dong, Z.-J., Yan, R.-Y., Wu, X.-Q., Zhang, L., Ma, J., & Zeng, Y. (2020). Psychological distress in the shadow of the COVID-19 pandemic: Preliminary development of an assessment scale. Psychiatry Research, 291, 113202–113202. https://doi.org/10.1016/j.psychres.2020.113202CrossRefPubMedPubMedCentral

33.

Arı, E., Yilmaz, V., & Arıkan, I. (2020). The effect of perceived risk of COVID-19 on anxiety: Developing a scale. Integrative Journal of Medical Sciences, 7, 1–7. https://doi.org/10.15342/ijms.7.190CrossRef

34.

Nikčević, A. V., & Spada, M. M. (2020). The COVID-19 anxiety syndrome scale: Development and psychometric properties. Psychiatry Research, 292, 113322. https://doi.org/10.1016/j.psychres.2020.113322CrossRefPubMedPubMedCentral

35.

Kira, I. A., Shuwiekh, H. A. M., Rice, K. G., Ashby, J. S., Elwakeel, S. A., Sous, M. S. F., Alhuwailah, A., Baali, S. B. A., Azdaou, C., Oliemat, E. M., & Jamil, H. J. (2021). Measuring COVID-19 as traumatic stress: Initial psychometrics and validation. Journal of Loss and Trauma, 26(3), 220–237. https://doi.org/10.1080/15325024.2020.1790160CrossRef

36.

Mira, J. J., Cobos, A., Martínez García, O., Bueno Domínguez, M. J., Astier-Peña, M. P., Pérez Pérez, P., Carrillo, I., Guilabert, M., Perez-Jover, V., Fernandez, C., Vicente, M. A., Lahera-Martin, M., Silvestre Busto, C., Lorenzo Martínez, S., Sanchez Martinez, A., Martin-Delgado, J., Mula, A., Marco-Gomez, B., Abad Bouzan, C., … Aranaz-Andres, J. (2021). An acute stress scale for health care professionals caring for patients with COVID-19: Validation study. JMIR Formative Research, 5(3), e27107. https://doi.org/10.2196/27107CrossRefPubMedPubMedCentral

37.

Zurlo, M. C., Cattaneo Della Volta, M. F., & Vallone, F. (2020). COVID-19 student stress questionnaire: Development and validation of a questionnaire to evaluate students’ stressors related to the coronavirus pandemic lockdown. Frontiers in Psychology, 11, 576758–576758. https://doi.org/10.3389/fpsyg.2020.576758CrossRefPubMedPubMedCentral

38.

Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S., Lahiri, D., & Lavie, C. J. (2020). Psychosocial impact of COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 779–788. https://doi.org/10.1016/j.dsx.2020.05.035CrossRef

39.

Wu, T., Jia, X., Shi, H., Niu, J., Yin, X., Xie, J., & Wang, X. (2021). Prevalence of mental health problems during the COVID-19 pandemic: A systematic review and meta-analysis. Journal of Affective Disorders, 281, 91–98. https://doi.org/10.1016/j.jad.2020.11.117CrossRefPubMed

40.

Kuderer, N. M., Choueiri, T. K., Shah, D. P., Shyr, Y., Rubinstein, S. M., Rivera, D. R., Shete, S., Hsu, C.-Y., Desai, A., de Lima Lopes, G., Grivas, P., Painter, C. A., Peters, S., Thompson, M. A., Bakouny, Z., Batist, G., Bekaii-Saab, T., Bilen, M. A., & Bouganim, N. (2020). Clinical impact of COVID-19 on patients with cancer (CCC19): A cohort study. The Lancet, 395(10241), 1907–1918. https://doi.org/10.1016/S0140-6736(20)31187-9CrossRef

41.

Williamson, E. J., Walker, A. J., Bhaskaran, K., Bacon, S., Bates, C., Morton, C. E., Curtis, H. J., Mehrkar, A., Evans, D., Inglesby, P., Cockburn, J., McDonald, H. I., MacKenna, B., Tomlinson, L., Douglas, I. J., Rentsch, C. T., Mathur, R., Wong, A. Y. S., & Grieve, R. (2020). Factors associated with COVID-19-related death using OpenSAFELY. Nature, 584(7821), 430–436. https://doi.org/10.1038/s41586-020-2521-4CrossRefPubMedPubMedCentral

42.

Ahmed, S. (2023). Cancer Care during the COVID-19 Pandemic: Challenges and adaptations. Current Oncology, 30(1), 45–47. https://doi.org/10.3390/curroncol30010004CrossRef

43.

von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., & Vandenbroucke, J. P. (2008). The strengthening the reporting of observational studies in epidemiology (STROBE) statement: Guidelines for reporting observational studies. Journal of Clinical Epidemiology, 61(4), 344–349. https://doi.org/10.1016/j.jclinepi.2007.11.008CrossRef

44.

Ponce-Blandon, J. A., Jimenez-Garcia, V. M., Romero-Castillo, R., Pabon-Carrasco, M., Jimenez-Picon, N., & Calabuig-Hernandez, R. (2021). Anxiety and perceived risk in red cross volunteer personnel facing the coronavirus disease 2019 pandemic. Frontiers in Psychology, 12, 720222. https://doi.org/10.3389/fpsyg.2021.720222CrossRefPubMedPubMedCentral

45.

Matsuishi, K., Kawazoe, A., Imai, H., Ito, A., Mouri, K., Kitamura, N., Miyake, K., Mino, K., Isobe, M., Takamiya, S., Hitokoto, H., & Mita, T. (2012). Psychological impact of the pandemic (H1N1) 2009 on general hospital workers in Kobe. Psychiatry and Clinical Neuroscience, 66(4), 353–360. https://doi.org/10.1111/j.1440-1819.2012.02336.xCrossRef

46.

Pollari, C. D., Brite, J., Brackbill, R. M., Gargano, L. M., Adams, S. W., Russo-Netzer, P., Davidov, J., Banyard, V., & Cone, J. E. (2020). World trade center exposure and posttraumatic growth: Assessing positive psychological change 15 years after 9/11. International Journal of Environmental Research and Public Health, 18(1), 104. https://doi.org/10.3390/ijerph18010104CrossRefPubMedPubMedCentral

47.

R Studio Team. (2022). Rstudio: Integrated development for r. In RStudio, Inc.

48.

Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2021). Semtools: Useful tools for structural equation modeling. R package version 0.5–5. In https://CRAN.R-project.org/package=semTools

49.

Rosseel, Y. (2012). Lavaan: An r package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02CrossRef

50.

Revelle, W. (2019). Psych: Procedures for personality and psychological research, version = 1.9.12. In Northwestern University. https://CRAN.R-project.org/package=psych

51.

Fletcher, T. D. (2012). Quantpsyc: Quantitative psychology tools. R package version 1.5. In https://CRAN.R-project.org/package=QuantPsyc

52.

Chalmers, R. P., & Flora, D. B. (2015). Faoutlier: An r package for detecting influential cases in exploratory and confirmatory factor analysis. Applied Psychological Measurement, 39(7), 573–574. https://doi.org/10.1177/0146621615597894CrossRefPubMedPubMedCentral

53.

Zhang, G., Jiang, G., Hattori, M., & Trichtinger, L. (2020). Efautilities: Utility functions for exploratory factor analysis. R package version 2.1.1. In https://CRAN.R-project.org/package=EFAutilities

54.

Lê, S., Josse, J., & Husson, F. (2008). Factominer: An r package for multivariate analysis. Journal of Statistical Software, 25(1), 1–18. https://doi.org/10.18637/jss.v025.i01CrossRef

55.

Raiche, G., & D., M. (2020). Nfactors: Parallel analysis and other non graphical solutions to the Cattell scree test. R package version 2.4.1. https://CRAN.R-project.org/package=nFactors

56.

Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed.). Wiley.CrossRef

57.

Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581–592. https://doi.org/10.1093/biomet/63.3.581CrossRef

58.

Newman, D. A. (2014). Missing data: Five practical guidelines. Organizational Research Methods, 17(4), 372–411. https://doi.org/10.1177/1094428114548590CrossRef

59.

Watkins, M. W. (2021). A step-by-step guide to exploratory factor analysis with R and Rstudio. Routledge.CrossRef

60.

Shi, D., Lee, T., Fairchild, A. J., & Maydeu-Olivares, A. (2020). Fitting ordinal factor analysis models with missing data: A comparison between pairwise deletion and multiple imputation. Educational and Psychological Measurement, 80(1), 41–66. https://doi.org/10.1177/0013164419845039CrossRefPubMed

61.

Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2019). Using multivariate statistics (7th ed.). Pearson.

62.

Enders, C. K. (2017). Multiple imputation as a flexible tool for missing data handling in clinical research. Behaviour Research and Therapy, 98, 4–18. https://doi.org/10.1016/j.brat.2016.11.008CrossRefPubMed

63.

Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67.CrossRef

64.

Van Buuren, S. (2018). Flexible imputation of missing data (2nd ed.). Chapman & Hall/CRC.CrossRef

65.

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley.CrossRef

66.

Bodner, T. E. (2008). What improves with increased missing data imputations? Structural Equation Modeling: A Multidisciplinary Journal, 15(4), 651–675. https://doi.org/10.1080/10705510802339072CrossRef

67.

White, I. R., Royston, P., & Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30, 377–399. https://doi.org/10.1002/sim.4067CrossRefPubMed

68.

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.

69.

Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530. https://doi.org/10.1093/biomet/57.3.519CrossRef

70.

Finney, S. J., & DiStefano, C. (2013). Nonnormal and categorical data in structural equation modeling. Structural equation modeling: A second course (2nd ed., pp. 439–492). IAP Information Age Publishing.

71.

Mueller, R. O., & Hancock, G. R. (2019). Structural equation modeling. The reviewer’s guide to quantitative methods in the social sciences (2nd ed., pp. 445–456). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781315755649-33CrossRef

72.

Terwee, C. B., Bot, S. D., de Boer, M. R., van der Windt, D. A., Knol, D. L., Dekker, J., Bouter, L. M., & de Vet, H. C. (2007). Quality criteria were proposed for measurement properties of health status questionnaires. Journal of Clinical Epidemiology, 60(1), 34–42. https://doi.org/10.1016/j.jclinepi.2006.03.012CrossRefPubMed

73.

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185. https://doi.org/10.1007/BF02289447CrossRefPubMed

74.

Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In V. Shal (Ed.), Problems and solutions in human assessment: Honoring Douglas N. Jackson at seventy (pp. 41–71). Kluwer Academic. https://doi.org/10.1007/978-1-4615-4397-8_3CrossRef

75.

Baglin, J. (2014). Improving your exploratory factor analysis for ordinal data: a demonstration using factor. Practical Assessment Research and Evaluation. https://doi.org/10.7275/dsep-4220CrossRef

76.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272CrossRef

77.

Morin, A. J. S., Myers, N. D., & Lee, S. (2020). Modern factor analytic techniques. In G. Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology (4th ed., pp. 1044–1073). Wiley.CrossRef

78.

Norman, G. R., & Streiner, D. L. (2014). Biostatistics: The bare essentials (4th ed.). People’s Medical Publishing House-USA.

79.

Watson, J. C. (2017). Establishing evidence for internal structure using exploratory factor analysis. Measurement and Evaluation in Counseling and Development, 50(4), 232–238. https://doi.org/10.1080/07481756.2017.1336931CrossRef

80.

Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press.

81.

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118CrossRef

82.

Kenny, D. A. (2020). Measuring model fit. http://davidakenny.net/cm/fit.htm

83.

Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., Bouter, L. M., & de Vet, H. C. (2010). The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. Journal of Clinical Epidemiology, 63(7), 737–745. https://doi.org/10.1016/j.jclinepi.2010.02.006CrossRefPubMed

84.

Hays, R. D., & Hayashi, T. (1990). Beyond internal consistency reliability: Rationale and user’s guide for multitrait analysis program on the microcomputer. Behavior Research Methods, Instruments & Computers, 22(2), 167–175. https://doi.org/10.3758/BF03203140CrossRef

85.

Ursachi, G., Horodnic, I. A., & Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679–686. https://doi.org/10.1016/S2212-5671(15)00123-9CrossRef

86.

Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer [Review]. Frontiers in Public Health, 6, 149. https://doi.org/10.3389/fpubh.2018.00149CrossRefPubMedPubMedCentral

87.

Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700CrossRefPubMed

88.

Ruscio, J., & Roche, B. (2012). Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychological Assessment, 24(2), 282. https://doi.org/10.1027/1614-2241/a000034CrossRefPubMed

89.

Marsh, H. W., Wen, Z., & Hau, K. T. (2004). Structural equation models of latent interactions: Evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9(3), 275. https://doi.org/10.1037/1082-989X.9.3.275CrossRefPubMed

90.

Widaman, K. F., Ferrer, E., & Conger, R. D. (2010). Factorial invariance within longitudinal structural equation models: Measuring the same construct across time. Child Development Perspectives, 4(1), 10–18. https://doi.org/10.1111/j.1750-8606.2009.00110.xCrossRefPubMedPubMedCentral

91.

Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2), 186–192. https://doi.org/10.1177/002224378802500207CrossRef

92.

Fields, J. F., Hunter-Childs, J., Tersine, A., Sisson, J., Parker, E., Velkoff, V., Logan, C., & Shin, H. (2020). Design and operation of the 2020 household pulse survey. U. S. C. Bureau. https://www2.census.gov/programs-surveys/demo/technical-documentation/hhp/2020_HPS_Background.pdf

93.

Lee, K. A., Ma, W., Sikavi, D. R., Drew, D. A., Nguyen, L. H., Bowyer, R. C. E., Cardoso, M. J., Fall, T., Freidin, M. B., Gomez, M., Graham, M., Guo, C.-G., Joshi, A. D., Kwon, S., Lo, C.-H., Lochlainn, M. N., Menni, C., Murray, B., & Mehta, R. (2021). Cancer and risk of COVID-19 through a general community survey. The Oncologist, 26(1), e182–e185. https://doi.org/10.1634/theoncologist.2020-0572CrossRefPubMed

94.

Puricelli Perin, D. M., Elfström, K. M., Bulliard, J.-L., Burón, A., Campbell, C., Flugelman, A. A., Giordano, L., Kamineni, A., Ponti, A., Rabeneck, L., Saraiya, M., Smith, R. A., & Broeders, M. J. M. (2021). Early assessment of the first wave of the COVID-19 pandemic on cancer screening services: The international cancer screening network COVID-19 survey. Preventive Medicine, 151, 106642. https://doi.org/10.1016/j.ypmed.2021.106642CrossRefPubMedPubMedCentral

95.

Freimuth, V. S., Musa, D., Hilyard, K., Quinn, S. C., & Kim, K. (2014). Trust during the early stages of the 2009 H1N1 pandemic. Journal of Health Communication, 19(3), 321–339. https://doi.org/10.1080/10810730.2013.811323CrossRefPubMed

Titel: Development and initial psychometric evaluation of a COVID-related psychosocial experiences questionnaire for cancer survivors
Auteurs: Estefany Saez-Clarke
Amy K. Otto
Sarah Prinsloo
Akina Natori
Richard W. Wagner
Telma I. Gomez
Jewel M. Ochoa
Shelley S. Tworoger
Cornelia M. Ulrich
Cassandra A. Hathaway
Anita R. Peoples
Michael H. Antoni
Julienne E. Bower
Lorenzo Cohen
Frank J. Penedo
Publicatiedatum: 26-06-2023
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 12/2023
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-023-03456-4

Bohn Stafleu van Loghum

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Background

Purpose

Methods

Results

Conclusions

Log in om toegang te krijgen

Andere artikelen Uitgave 12/2023

Patient quality of life and caregiver experiences in ovarian cancer: How are they related?

Psychosocial factors associated with j-pouch surgery for patients with IBD: a scoping review

Development and evaluation of the Norwegian Fatigue Characteristics and Interference Measure (FCIM) for stroke survivors: cognitive interviews and Rasch analysis

Profiles of subjective well-being among people living with HIV during the COVID-19 pandemic

Impaired health-related quality of life in the HCV cure era: who is concerned? (ANRS CO22 HEPATHER French cohort)

A scoping review of the use of visual tools and adapted easy-read approaches in Quality-of-Life instruments for adults