Need Support and Regulatory Focus in Responding to COVID-19
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".