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Record W3181118230 · doi:10.1016/j.ijchp.2021.100256

An International Study on Psychological Coping During COVID-19: Towards a Meaning-Centered Coping Style

2021· article· en· W3181118230 on OpenAlexaff
Nikolett Eisenbeck, David F. Carreno, Paul T. P. Wong, Joshua A. Hicks, Ruíz-Ruano García María, Jorge López Puga, W. James Greville, Ines Testoni, Gianmarco Biancalani, Ana C Cepeda-Lopez, Sofía Villareal, Violeta Enea, Christian Schulz, Jonas Jansen, Murat Yıldırım, Gökmen Arslan, José Fernando A. Cruz, Rui Sofia, Maria José Ferreira, Farzana Ashraf, Grażyna Wąsowicz, Shahinaz M. Shalaby, Reham A. Amer, Hadda Yousfi, JohnBosco Chika Chukwuorji, Valeschka Martins Guerra, Sandeep Kumar Singh, Samantha J. Heintzelman, Bonar Hutapea, Bouchara Béjaoui, Arobindu Dash, Károly Kornél Schlosser, Malin Anniko, Martin Rossa, Hattaphan Wongcharee, Andreja Avsec, Gaja Zager Kocjan, Tina Kavčič, Dmitry Leontiev, Olga Taranenko, Е. Рассказова, Elizabeth A. Maher, José M. García‐Montes

Bibliographic record

VenueInternational Journal of Clinical and Health Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoping (psychology)PsychologyMental healthLonelinessAnxietyClinical psychologyDistressSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

This study examined the role of different psychological coping mechanisms in mental and physical health during the initial phases of the COVID-19 crisis with an emphasis on meaning-centered coping. A total of 11,227 people from 30 countries across all continents participated in the study and completed measures of psychological distress (depression, stress, and anxiety), loneliness, well-being, and physical health, together with measures of problem-focused and emotion-focused coping, and a measure called the Meaning-centered Coping Scale (MCCS) that was developed in the present study. Validation analyses of the MCCS were performed in all countries, and data were assessed by multilevel modeling (MLM). The MCCS showed a robust one-factor structure in 30 countries with good test-retest, concurrent and divergent validity results. MLM analyses showed mixed results regarding emotion and problem-focused coping strategies. However, the MCCS was the strongest positive predictor of physical and mental health among all coping strategies, independently of demographic characteristics and country-level variables. The findings suggest that the MCCS is a valid measure to assess meaning-centered coping. The results also call for policies promoting effective coping to mitigate collective suffering during the pandemic. Este estudio examinó el papel de diferentes estrategias de afrontamiento psicológico en la salud mental y física durante las fases iniciales de la crisis de COVID-19. 11,227 personas de 30 países representando todos los continentes participaron en el estudio y completaron medidas de malestar psicológico (depresión, estrés y ansiedad), soledad, bienestar, salud física, medidas de afrontamiento centrado en el problema y en la emoción, y una medida denominada Escala del Afrontamiento Centrado en el Sentido (MCCS) que fue desarrollada en este estudio. El análisis de validación de la MCCS se realizó en todos los países, y los datos se evaluaron mediante un modelo multinivel. La MCCS mostró una estructura unifactorial en 30 países con buenos resultados de validez test-retest, concurrente y divergente. Los análisis mostraron resultados mixtos en cuanto a las estrategias de afrontamiento centradas en la emoción y en el problema. La MCCS fue el predictor positivo más fuerte de salud física y mental, independientemente de las características demográficas y las variables a nivel de país. Los resultados sugieren que la MCCS es un insrumento fiable para medir afrontamiento centrado en el sentido. Estos resultados pueden servir para dirigir políticas que promuevan un afrontamiento eficaz con el fin de mitigar el sufrimiento colectivo durante la pandemia.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.444
GPT teacher head0.656
Teacher spread0.211 · 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 teacher head, not a consensus.

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".

Quick stats

Citations102
Published2021
Admission routes1
Has abstractyes

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