AN INTEGRATED TRANSITIONAL CARE MODEL FOR OLDER ADULTS WITH MULTIMORBIDITY AND DEPRESSIVE SYMPTOMS
Bibliographic record
Abstract
Abstract This pragmatic randomized controlled trial examined the implementation, effectiveness and costs of a nurse-led transitional care intervention to improve hospital-to-home transitions for 127 older adults (≥ 65 years) with depressive symptoms and multimorbidity in three Ontario communities. Participants were randomly allocated to receive the intervention plus usual care (n=63) or usual care alone (n=64). The intervention included an average of 5 in-home visits and 6 phone calls from a Registered Nurse (RN) over a 6-month period. The RN provided system navigation, patient education, medication review, and management of depressive symptoms and chronic conditions. Implementation outcomes included engagement rate, intervention dose, and feasibility of intervention implementation. Effectiveness outcomes included quality of life, depressive symptoms, anxiety, social support, and health and social service use and costs. Participants were an average of 76 years and had an average of 8 chronic conditions. Findings suggest that the intervention was feasible and acceptable to participants and providers. Intention-to-treat analyses using ANCOVA models showed no statistically significant group differences for the outcomes. However, the upper 95% confidence interval for the mean group difference showed greater clinically significant improvements in physical functioning in the intervention group. Quantile regression showed that the intervention may result in greater improvements in physical functioning for individuals with low to average physical functioning values compared to the control group. The intervention may also result in higher levels of perceived social support for individuals with a range of social support values. No statistically significant group differences were observed for service use or costs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".