Security motives and negative affective experiences during the early months of the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: Self-regulation can help individuals cope during stressful events, but little is known about why and when this might occur. We examined if being more focused on prevention was linked to negative affective experiences during the COVID-19 pandemic. We also examined possible underlying mechanisms for this association, and whether social support buffered it. DESIGN: = 1269). MAIN OUTCOME MEASURES: Regulatory focus and worry for health (T1), adherence to self-isolation and preventive health behaviours (T2), negative affective experiences, positive affect, frequency of online interactions, and perceived social support (T3). RESULTS: Prevention focus was associated with health worries at baseline and linked to greater adherence to preventive health behaviours (T2). Only adherence to self-isolation was linked to more negative affective experiences (T3). Exploratory analyses showed that prevention focus was linked to more negative affective experiences (T3), but only for participants with fewer online interactions with their family and less perceived social support from family and friends. CONCLUSIONS: Prevention motives in threatening times can be a double-edged sword, with benefits for health behaviours and consequences for negative affective experiences. Having a strong social network during these times can alleviate these consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".