Meaningful Activity, Psychosocial Wellbeing, and Poverty During COVID-19: A Longitudinal Study
Bibliographic record
Abstract
Background: Only a few studies have explored experiences of meaningful activity and associations with psychosocial wellbeing during COVID-19. None reflect a Canadian context or focus on persons living in poverty. Purpose: To identify experiences and associations between meaningful activity and psychosocial wellbeing for persons living in poverty during the first year of COVID-19. Method: We delivered a quantitative survey at three time points during the first year of the pandemic supplemented by qualitative interviews at Time(T) 1 and 1 year later at T3. Findings: One hundred and eight participants completed T1 surveys, and 27 participated in qualitative interviews. Several statistically significant correlations between indices of meaningful activity engagement and psychosocial wellbeing were identified across T1–T3. Meaningful activity decreased from T1–T3 [X 2 (2, n = 49) = 9.110, p < .05], with a significant decline from T2–T3 (z = −3.375, p < .001). In T1 qualitative interviews, participants indicated that physical distancing exacerbated exclusion from meaningful activities early in the pandemic. At T3 (1 year later), they described how classist and ableist physical distancing policies layered additional burdens on daily life. Implications: Meaningful activity engagement and psychosocial wellbeing are closely associated and need to be accounted for in the development of pandemic policies that affect persons living in low income. Occupational therapists have a key role in pandemic recovery.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".