Resilience in Residents: Understanding Mood Transitions Among Older Canadian Long-Term Care Residents During COVID-19
Bibliographic record
Abstract
OBJECTIVES: This study examined the transitions between different mood (depressive symptoms) states among residents of long-term care (LTC) homes during the first 2 waves of COVID-19. It also examined the transitions from these mood states to terminal clinical outcomes. DESIGN: A retrospective longitudinal analysis of older residents in Canadian LTC homes in 3 provinces from January 2010 to February 2021. SETTING AND PARTICIPANTS: Canadian LTC residents aged 65+ assessed in Alberta, British Columbia, and Ontario were divided into 2 cohorts: pre-COVID-19 (January 2010-February 2020) and COVID-19 (March 2020-February 2021), further divided into 2 subgroups: Wave 1 (March-August 2020) and Wave 2 (September-December 2020). Inclusion required admission during each period, a stay of at least 90 days, and either 2 assessments or 1 with discharge details. Residents admitted for fewer than 90 days or with only 1 assessment and no discharge data were excluded. METHODS: We used a 1-step Markov multistate transition model to examine probabilities in mood transitions and the associated factors with each transition. Our primary outcome of interest was transition in mood measured by the Depression Rating Scale, which is a proxy measure for mood. RESULTS: Our results suggest improved mood among surviving residents during the first 2 waves of COVID-19. Compared with the pre-pandemic period, residents were more likely to transition from mild depressive symptoms to no symptoms during wave 1 [odds ratio (OR), 1.06] and wave 2 (OR, 1.08), and from moderate-severe symptoms to no symptoms during wave 1 (OR, 1.14) and wave 2 (OR, 1.14). Regardless of baseline mood, residents were more likely to be discharged home or die, and less likely to be discharged to hospital during waves 1 and 2, compared with the pre-pandemic period. CONCLUSION AND IMPLICATIONS: COVID-19 may not have worsened LTC home residents' mood, contrary to other findings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".