Exploring Distress and Occupational Participation Among Older Canadians During the COVID-19 Pandemic
Bibliographic record
Abstract
Background. The coronavirus disease 2019 (COVID-19) pandemic disrupted daily life with corresponding implications on levels of distress. Purpose. To describe factors associated with high distress among community-dwelling older adults during the first lockdown and explore how occupational participation was managed. Methods. A mixed methods design whereby multivariate regression analysis of a survey ( N = 263) identified factors associated with high distress, as per the Impact of Events of Scale-Revised (IES-R). Follow-up interviews with a sub-sample of those surveyed who reflected a range of IES-R scores were conducted ( N = 32). Findings. Those with lower resilience and anxiety/depression had 6.84 and 4.09 greater odds respectively of high distress. From the interviews, the main theme, “Lost and Found,” and subthemes (Interruption and Disruption; Surving, not Thriving; Moving Forward, Finding Meaning) highlighted the process and corresponding stages, including adaptive strategies, by which participants navigated changes in their occupational participation. Implications. While the results suggest that many older adults, including those with high distress, were able to manage daily life under lockdown, some experienced ongoing challenges in doing so. Future studies should focus on those who experienced or who are at-higher risk for such challenges to identify supports that mitigate adverse consequences if another event of this magnitude occurs again.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".