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Record W3107149457 · doi:10.3389/fpsyg.2020.589446

Need Support and Regulatory Focus in Responding to COVID-19

2020· article· en· W3107149457 on OpenAlexaboutno aff
Leigh Ann Vaughn, Chase A. Garvey, Rachael D. Chalachan

Bibliographic record

VenueFrontiers in Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)PsychologyFocus (optics)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakRegulatory focus theoryCognitive psychologySocial psychologyVirologyMedicine

Abstract

fetched live from OpenAlex

Prevention focus is a self-regulatory orientation that serves the need for security, and promotion focus is a self-regulatory orientation that serves the need for growth. From mid-March to early April 2020, did people judge prevention focus to be more useful than promotion focus for responding to COVID-19? Our study tested and showed support for this hypothesis with 401 American and Canadian participants, who we sampled in 100-person waves on the first 4 Thursdays of the pandemic. For this study, we developed a new measure of the judged usefulness of promotion and prevention focus. Additionally, results showed that the judged usefulness of promotion and prevention focus related positively to support of the psychological needs for autonomy and relatedness, respectively, in responding to COVID-19. Exploratory analyses showed that day-to-day differences in autonomy, competence, and relatedness support and in promotion and prevention focus tended to be small, which is notable given the large-scale changes to social distancing, employment, and media coverage of the virus during this time. Our research could be useful for crafting persuasive advocacy and narrative communications that encourage social distancing to protect others about whom people care most.

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)0.001
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.076
GPT teacher head0.432
Teacher spread0.355 · 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 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

Citations13
Published2020
Admission routes1
Has abstractyes

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