Functional Recovery Within a Formal Home Care Program
Bibliographic record
Abstract
OBJECTIVE: To identify home care clients with substantial functional deficits who had capacity to improve and, thus, for whom recovery goals should be articulated. DESIGN: Retrospective longitudinal analysis of an international home care database. SETTING AND PARTICIPANTS: 523,907 persons receiving home care, having 2 assessments, on average, 8 months apart. MEASURES: Recovery algorithm variables included counts of dependencies of activities of daily living (ADL) and instrumental ADL (IADL) tasks, hospitalization in the last 30 days, functional decline in the last 90 days, and self-belief in one's capacity to improve. Primary dependent variable was improvement in the IADL-ADL Functional Hierarchy Scale. RESULTS: The Recovery Algorithm has 7 graded levels: the top 3 represent approximately 9% of home care clients, whereas the bottom level (where recovery is least likely to occur) includes 60% of home care clients (many with higher counts of extensive ADL or IADL dependencies). The improvement rates rise from 6.9% to 47.2% across the 7 levels of the algorithm. This relationship between change in IADL-ADL Functional Hierarchy Scale scores and Recovery Algorithm levels remained strong across age categories and cognitive performance levels. Higher rates of improvement occurred for persons who received physical therapy. CONCLUSIONS/IMPLICATIONS: The Recovery Algorithm is based on a mix of positive risk indicators and the person's challenged baseline functional status. For persons with higher scores on the algorithm, recovery is expected and should be considered in care plan goals. In addition, use of physical therapy increases the probability of recovery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".