Perceived stress and occupation-based coping strategies during the first wave of the COVID-19 pandemic
Bibliographic record
Abstract
Background: The COVID-19 pandemic caused considerable stress. Occupations may play an important role in decreasing stress; therefore, this study examined stress and occupation-based coping strategies used during the first wave of the COVID-19 pandemic in 2020.Methods: Data were analyzed from a Canadian cross-sectional survey that included retrospective and current measures of stress, and an open-ended question regarding how participants coped during the first wave of the COVID-19 pandemic. Text responses were categorized using Skinner’s stress and coping framework, and the Do-Live-Well framework; and examined the association between activating the body, mind, and senses-type occupations and stress, controlling for pre-pandemic stress and other potential confounders.Results: The 1,473 participants were primarily women (74.7%). All participants identified at least one occupation-based strategy that was categorized as ‘distraction’ in Skinner’s framework. When further classified using the Do-Live-Well framework, most occupation-based strategies related to activating the body, mind, and senses (64.3%). Bivariate/correlational analyses demonstrated relationships between stress and pre-pandemic stress (τ=0.65); annual income less than 30,000 CAD (τ=0.10); being employed (τ=0.11); postsecondary education (τ=0.09); and having a minor child at home (τ=0.11). No association was found when the relationship between stress and activating the body, mind, and senses was tested in a multivariate model containing these potential confounders. The most robust model contained only pre-pandemic stress.Conclusion: Occupation-based strategies were frequently used for coping during the first wave of the pandemic. Further examination of the effectiveness of these strategies, using appropriate frameworks and methods, is warranted.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".