Autonomy support, emotion regulation, and subjective well-being during the omicron period of the COVID-19 crisis
Bibliographic record
Abstract
The COVID-19 crisis highlighted significant differences in individual resilience and susceptibility to psychological stress. This eight-month longitudinal study, involving 535 community adults (58 % female, M age = 43.97), examines the intricate relationship between social support and well-being during periods of uncertainty. Grounded in Self-Determination Theory, the research explores how perceived autonomy support from close relationships—marked by active listening and empathetic perspective-taking—affects psychological need satisfaction (autonomy, competence, and relatedness), emotion regulation (integrative regulation, emotional dysregulation, and emotional suppression), and subjective well-being. The findings indicate that autonomy support is significantly related to enhanced psychological need satisfaction, integrative regulation, and subjective well-being. Additionally, psychological need satisfaction and integrative regulation mediated the relationship between autonomy support and subjective well-being. These results emphasize the critical role of autonomy-supportive relationships in promoting psychological well-being during challenging times. The study offers valuable practical implications for fostering autonomy support in close relationships and lays a foundation for future research in this area.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".