MAKING THE TRANSITION FROM RESIDENTIAL LONG-TERM CARE (RLTC) BACK TO THE COMMUNITY
Bibliographic record
Abstract
The objective of this research is to examine clinical (e.g., older adult’s physical wellbeing) and system-level (e.g., use of community-based services) outcomes related to older adults’ transition from RLTC back to the community. This study is a longitudinal design and uses data from the RAI MDS 2.0 from a cohort of older adults residing in RLTC in one health region in British Columbia, Canada. The purpose of this presentation is to report findings on the application of an algorithm designed to identify older RLTC residents who might be candidates for a transition back to the community. The algorithm was evaluated on a cohort of older adults who had been discharged from RLTC (N=3,859) between the years 2010 and 2014. A small percentage of these residents (n = 102) had been transitioned back to the community, whereas non-transitioning residents (n = 3,757) either moved to another RLTC or died. Statistically significant differences were observed across several variables; compared to non-transitioners, transitioners were younger, had been in RLTC for shorter durations, and were more likely to express a preference to return to the community and to have a support person who was positive about the transition. An ROC analysis of the algorithm disclosed a significant area under the curve (c = .767, p < .001). These findings suggest that a small portion of older adults could transition back to the community. This has important implications for the development of policies that ensure appropriate and accessible health services in the community.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".