Incidence of New Mood Symptoms Among Residents in Canadian Long-Term Care Homes During the COVID-19 Pandemic: A Longitudinal Analysis
Bibliographic record
Abstract
Objective To evaluate the impact of the COVID-19 pandemic on the risk of experiencing new symptoms of depression among Canadian long-term care (LTC) home residents. Design and setting Retrospective longitudinal study of interRAI MDS 2.0 comprehensive health assessments completed in Alberta, British Columbia, Manitoba, Ontario, and Newfoundland, Canada between March 1, 2019, and June 30, 2020. Participants About 22,441 residents without a diagnosis of depression and a Depression Rating Scale score of zero on their baseline assessment completed in the pandemic (March 2020) and prepandemic (March 2019) comparison period. Outcomes Incidence and severity of new depressive symptoms recorded on routine three-month follow-up assessment . Results The likelihood of developing new depressive symptoms was greater during the pandemic period (adjusted proportional odds ratio [aOR] 1.16 [95% CI 1.08–1.24]). In province-specific models, residents in Alberta (aOR 1.26, 95% CI 1.05–1.51), British Columbia (aOR 1.33, 95% CI 1.15–1.55), and Ontario (aOR 1.11, 95% CI 1.02–1.21) were more likely to develop new depressive symptoms during the pandemic period. Conclusion and Implications The initial waves of the pandemic likely contributed to increased mental health challenges due to various factors. Our findings highlight the need for comprehensive interventions to support all residents' mental well-being during outbreaks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".