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Record W3191014628 · doi:10.22126/jap.2021.6167.1504

The Coronavirus Anxiety in the Elderly: The Role of Coping Styles with Stress and Meta-Worry

2021· article· en· W3191014628 on OpenAlexaboutno aff
Maryam Rahimyan, Zahra Dasht Bozorgi

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsWorryAnxietyCoping (psychology)Coronavirus disease 2019 (COVID-19)Clinical psychologyPsychologyCoronavirusMedicinePsychiatryInternal medicineInfectious disease (medical specialty)Disease

Abstract

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Coronavirus symptoms have more severe physical and psychological clinical manifestations in the elderly. Accordingly, the elderly seem to be one of the main groups at risk for Coronavirus disease. The aim of this study was to predict Coronavirus anxiety based on coping styles with stress and meta-worry. The research design was correlational. The statistical population included all the elderly living in Ahvaz, among whom 300 were selected as the study sample using online convenience sampling method. The research instruments included Alizadeh et al.'s Corona Anxiety, Lazarus and Falkman's Coping Styles, and Wells's meta-worry Inventory. Used to analyze the data was a multiple regression method. The results indicated that problem-oriented coping style (r-=0.401) had a negative and significant relationship with corona anxiety in the elderly and emotion-centered coping style (r=0.281) and meta-worry (r=0.429) have a positive and significant relationship with corona anxiety (p < 0.01). Besides, the variables of problem-oriented coping style, emotion-centered coping style and meta-worry were able to predict 31.5% of the changes in corona anxiety in the elderly, which the share of meta-worry was more than other variables (p < 0.01). Considering the capability of coping style and meta-worry in predicting Coronavirus anxiety in the elderly, training programs can be designed and represented through virtual workshops to increase problem-oriented coping style and to decrease emotion-centered coping style and meta-worry, in order to reduce Coronavirus anxiety. References Aldwin, C. M., Molitor, N. T., Avron, S., Levenson, M. R., Molitor, J., & Igarashi, H. (2011). Do stress trajectories predict mortality in older men? Longitudinal findings from the VA normative aging study. Journal of Aging Research, 11(1), Article 896109. Alipour, A., Ghadami, A., Alipour, Z., Abdollahzadeh, H. (2020). Preliminary validation of the Corona Disease Anxiety Scale (CDAS) in the Iranian sample. Quarterly Journal of Health Psychology and Social Behavior, 8(32), 163-175. [Persian]. Allahtavakoli, M. (2020). Coping with stress of COVID_19 epidemic. Journal of Jiroft University of Medical Sciences, 7(1), 253-254. [Persian] Amirfakhraei, A., Masoumifard, M., Esmaeilishad, B., DashtBozorgi, Z., & Darvish Baseri, L. (2020). Prediction of corona virus anxiety based on health concern, psychological hardiness, and positive meta-emotion in diabetic patients. Journal of Diabetes Nursing, 8(2), 1072-1083. [Persian] Armitage, R., & Nellums, L. B. (2020). COVID-19 and the consequences of isolating the elderly. The Lancet Public Health, 5(5), Article e256. Bajema, K. L., Oster, A. M., McGovern, O. L., Lindstrom, S., Stenger, M. R., Anderson, T. C., Isenhour C, Clarke KR, Evans ME, Chu VT, Biggs HM, Oliver, S. E. (2020). Persons evaluated for 2019 novel coronavirus—United States, January 2020. Morbidity and Mortality Weekly Report, 69(6), 166-170. Bhutani, S., & Greenwald, B. (2021). Loneliness in the elderly during the COVID-19 pandemic: A literature review in preparation for a future study. The American Journal of Geriatric Psychiatry, 29(4), S87-S88. Borkovec, T. D., & Roemer, L. (1995). Perceived functions of worry among generalizedanxiety subjects: Distraction from more emotionally distressing topics? BehaviourTherapy and Experimental Psychiatry, 26(1), 25-30. Brooks, S. K., Webster, R. K., Smith, L. E., Woodland, L., Wessely, S., Greenberg, N., Rubin, G. J. (2020). The Psychological impact of quarantine and how to reduce it: Rapid review of the evidence. Rapid Review, 39(10), 912-920 Chen Q, Liang M, Li Y, Guo J, Fei D, Wang L, et al. (2020). Mental health care for medical staff in China during the COVID-19 outbreak. The Lancet Psychiatry, 7(4): e15-e6. Chen, Y., Liang, Y., Zhang, W., Crawford, J. C., Sakel, K.L., & Dong, X. (2019). Perceived stress and cognitive decline in Chinese-American older adults. Journal of the American Geriatrics Society, 67(3), 519-524. Dong X., Wang L., Tao Y., Suo X., Li Y., Liu F., Zhao Y., Zhang Q. (2017). Psychometric properties of the anxiety inventory for respiratory disease in patients with COPD in China. International Journal of Chronic Obstructive Pulmonary Disease, 12, 49-58. Dousti, P., Hosseininia, N., Ghodrati, G., & Ebrahimi, M. (2020). Comparison of rumination, Sense of helplessness, and magnification at different ages and their relation to stress coping styles in visitors of a mental health monitoring website during the first week of coronavirus outbreaks. Knowledge & Research in Applied Psychology, 21(2), 105-114. [Persian] Fata, L., Motabi, F., Moloudi, R., & Ziyaei, K. (2010). Psychometric properties of Persian version of thought control questionnaire and anxiety thoughts questionnaire in Iranian students. Journal of Psychological Models and Methods, 1(1), 81-103. [Persian] Huang, L., Lei, W., Xu, F., Liu, H., & Yu, L. (2020). Emotional responses and coping strategies in nurses and nursing students during Covid-19 outbreak: A comparative study. PLos ONE, 15(8), Article e0237303. Jones-Bitton, A., Best, C., MacTavish, J., Fleming, S., & Hoy, S. (2020). Stress, anxiety, depression, and resilience in Canadian farmers. Social Psychiatry and Psychiatric Epidemiology, 55, 229-236. Lai, C. C., Shih, T. P., Ko, W. C., Tang, H. J., & Hsueh, P. R. (2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. International Journal of Antimicrobial Agents, 55(3), 105924. Lazarus, R. S. (1991). Progress on a cognitive-motivational-relational theory of emotion. American psychologist, 46(8), 819. Lazarus, R. S., Folkman, S. (2000). Stress apprasial and coping. New York: Springer. Leung, C. (2020). Clinical features of deaths in the novel coronavirus epidemic in China. Reviews in Medical Virology, 30(3), 1-4. Li, S., Wang, Y., Xue, J., Zhao, N., & Zhu, T. (2020). The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users. International Journal of Environmental Research and Public Health, 17(6), Article 2032. Mori, H., Obinata, H., Murakami, W., Tatsuya, K., Sasaki, H., Miyake, Y., Taniguchi Y, Ota S, Yamaga M, Suyama Y, Tamura, K. (2020). Comparison of COVID-19 disease between young and elderly patients: Hidden viral shedding of COVID-19. Journal of Infection and Chemotherapy, 27(1), 70-75. Narimani, M., & Eyni, S. (2021). The causal model of coronavirus anxiety in the elderly based on perceived stress and sense of cohesion: The mediating role of perceived social support. Aging Psychology, 7(1), 13-27. Ouanes, S., Kumar, R., Doleh, E. S. I., Smida, M., Al-Kaabi, A., Al-Shahrani, A. M., Mohamedsalih, G. A., Ahmed, N. E., Assar, A., Khoodoruth, M. A. S., AbuKhattab, M., Al Maslamani, M., AlAbdulla, M. A. (2021). Mental Health, resilience, and religiosity in the elderly under COVID-19 quarantine in Qatar. Archives of Gerontology and Geriatrics, 96, 104457. Pearman, A., Hughes, M. L., Smith, E. L., Neupert, S. D. (2020). Age differences in risk and resilience factors in COVID-19-related stress. Journal of Gerontology: Psychological Sciences, 76(2), 38-44. Qi, A., & Dada, D. (2021). Impact of COVID-19 on mental health in the elderly population. The American Journal of Geriatric Psychiatry, 29(4), S84-S85. Shadmehr, M., Ramak, N., Sangani, A. (2020). The role of prceived mental stress in the health of suspected cases of COVID-19. Journal of Military Medicine, 22(2), 115-121. [Persian] To, K. K. W., Tsang, O. T. Y., Yip, C. C. Y., Chan, K. H., Wu, T. C., Chan, J. M. C., Leung WS, Chik TS, Choi CY, Kandamby DH, Lung DC. (2020). Consistent detection of 2019 novel coronavirus in saliva. Clinical Infectious Diseases, 71(15), 841-843. Vahia, I. V., Blazer, D. G., Smith, G. S., Karp, J. F., Steffens, D. C., Forester, B. P., Tampi, R., Agronin, M., Jeste, D. V., & Reynolds, C. F. (2020). COVID-19, mental health and aging: A need for new knowledge to bridge science and service. American Journal of Geriatric Psychiatry, 28(7), 695-697. Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., & Zhao, Y. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Journal of Mental Health, 323(11), 1061–1069. Wells, A. (2010). Emotional disorders and metacognition: innovative cognitive therapy. Chichester, UK: Wiley. Wells, A., & Davies, M. I. (1994). The thought control questionnaire: A measure of individual differences in the control of unwanted thoughts. Behaviour Research and Therapy, 32(8), 871-878. Wells, A., & Papageorgiou, C. (2008). Worry and the incubation of intrusive imagesfollowing stress. Behavior Research and Therapy, 33(5), 579–583. Westerhuis, W., Zijlmans, M., Fischer, K., van Andel, J., & Leijten, F. S. (2011). Coping style and quality of life in patients with epilepsy: a cross-sectional study. Journal of Neurology, 258(1), 37-43. Zhu, H., Wei, L., & Niu, P. (2020). The novel coronavirus outbreak in Wuhan, China. Global Health Research and Policy, 5(1), 1-3.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.335
GPT teacher head0.592
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2021
Admission routes1
Has abstractyes

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