Factors Associated with Mood Transitions among Older Canadian Long-Term Care Residents: A Multistate Transition Model
Bibliographic record
Abstract
OBJECTIVES: This study examines the complex transitions between the different mood states and absorbing states out of long-term care settings, as well as the factors affecting those transitions. DESIGN: A retrospective longitudinal analysis of older residents in Canadian long-term care homes in 3 provinces. SETTING AND PARTICIPANTS: Residents residing in long-term care homes in 3 Canadian provinces (Alberta, British Columbia, and Ontario) over a 10-year period from January 2010 to February 2020, with an age of at least 65. METHODS: We used a 1-step Markov multistate transition model to examine transitions in mood over time as well as the factors affecting those transitions using the standardized interRAI MDS 2.0 comprehensive health assessment. The MDS 2.0 assessments are completed by trained assessors within 2 weeks of the resident's admission. RESULTS: Our results showed that 46% of residents initially present with no mood disturbance on admission and 31% with mild mood disturbance on admission and 23% with moderate/severe mood disturbance on admission. Factors associated with worsening of mood include aggressive behavior; health instability; impaired cognition; major comorbidities; pain or poor sleep; conflict with family, friends, or other residents; and anxiety. Of the facility-level attributes, Alberta was associated with worsening of mood. CONCLUSIONS AND IMPLICATIONS: Our study identified key factors influencing mood transitions, highlighting pain and aggressive behavior as significant contributors to worsening mood, both of which are modifiable through targeted interventions. The findings suggest substantial opportunities for mood improvement in long-term care settings.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".