Contributions of occupational purpose and type to well-being during the COVID-19 pandemic: A cross-sectional study
Bibliographic record
Abstract
Background: The COVID-19 pandemic provided an opportunity to gather empirical evidence about the role of occupation and occupational purpose in promoting well-being. Drawing upon previously defined occupational purposes—survival, diversion, mastery, habit, support, identity, and spirituality—we sought to examine the role of occupation in promoting well-being during the COVID-19 pandemic and how occupation exerted its effect.Methods: The study used cross-sectional time use diary survey data, which included assigning one purpose to occupations selected, and measures for well-being. Diaries for 165 participants were included. Data were gathered from November 2020 to February 2021. Descriptive statistics were generated for time spent in each occupation type and purpose, and occupations associated with each purpose. Multivariate modelling was used to describe the relationship between well-being and time spent in each occupation and purpose.Results: The most common purposes were survival and habit. For 34% of participants, occupations undertaken for mastery occupied a significant portion of the day, while occupations that provided diversion were identified by 79% but took less time. The average well-being score was 138.1/300. Modelling showed that well-being increased significantly with time spent in occupations undertaken by habit (p = .004) and time spent in eating (p = .008); and decreased with time spent in adult care (p = .036). Notably, eating, specifically for habit, contributed to well-being.Conclusions: The variations in how people ascribed purpose to occupations highlight the individual nature of occupational purpose. Promoting occupations associated with daily habits may mitigate the effects of stressful situations.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".