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Record W4200134834 · doi:10.31234/osf.io/6j38h

The Role of Values in Coping with Health and Economic Threats of COVID-19

2021· preprint· en· W4200134834 on OpenAlexfundno aff
Edward P. Lemay, Arie W. Kruglanski, Erica Molinario, Maximilian Agostini, Jocelyn J. Bélanger, Ben Gützkow, Jannis Kreienkamp, Michelle R. vanDellen, PsyCorona team, N. Pontus Leander

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsnot available
FundersEuropean Regional Development FundYork UniversityNederlandse Organisatie voor Wetenschappelijk OnderzoekNew York University Abu DhabiRijksuniversiteit GroningenInstituto de Salud Carlos III
KeywordsPandemicPrioritizationCoronavirus disease 2019 (COVID-19)Coping (psychology)PsychologyAgency (philosophy)Social psychology2019-20 coronavirus outbreakPolitical scienceSociologyBusinessMedicineInfectious disease (medical specialty)DiseaseClinical psychologyVirology

Abstract

fetched live from OpenAlex

The current research examined the role of values in guiding people’s responses to COVID-19. Results from an international study involving 115 countries (N = 61,490) suggest that health and economic threats of COVID-19 evoke different values, with implications for controlling and coping with the pandemic. Specifically, health threats evoked prioritization of communal values related to caring for others and belonging, whereas economic threats predicted prioritization of agentic values focused on competition and achievement. Concurrently and over time, prioritizing communal values over agentic values was associated with enactment of prevention behaviors that reduce virus transmission, motivations to help others suffering from the pandemic, and positive attitudes toward outgroup members. These results, which were generally consistent across individual and national levels of analysis, suggest that COVID-19 threats may indirectly shape important responses to the pandemic through their influence on people’s prioritization of communion and agency. Theoretical and practical implications are discussed.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.119
GPT teacher head0.345
Teacher spread0.225 · 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 designNot applicable
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

Citations2
Published2021
Admission routes1
Has abstractyes

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