Coping during COVID-19: The Impact of Cognitive Appraisal on Problem Orientation, Coping Behaviors, Body Image, and Perceptions of Eating Behaviors and Physical Activity during the Pandemic
Bibliographic record
Abstract
Large surveys indicate that many people perceive that their health behaviors (i.e., eating behaviors, physical activity, and self-care routines) and body image have changed during COVID-19; however, large individual variation exists. A person’s cognitive appraisal of COVID-19 disruptions may help account for individual differences. Those with a negative problem orientation perceive problems as “threats”, whereas those with a positive problem orientation reframe problems as “opportunities”. The present experimental study examined the impact of appraisals, specifically being prompted to reflect on the changes in health routines precipitated by COVID-19 restrictions as either “threats” or “opportunities”, on problem orientation, coping behaviours, body image, and perceptions of eating behaviors and physical activity in a sample of female undergraduate students (N = 363). The group that reflected on challenges/barriers reported having a more negative problem orientation, being more negatively impacted by COVID-19, engaging in more maladaptive coping behaviors, and having less positive body image compared to participants who reflected on opportunities presented during the pandemic. Findings suggest that appraisals and problem orientation are malleable, and that people who tend to fixate on the challenges associated with COVID-19 may benefit from strategically reflecting on their own resilience and new opportunities that have arisen for engaging in health behaviors.
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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".