In-Home Rehabilitation Resources and Avoidable Admissions to Inpatient Rehabilitation after Stroke: An Ecological Study
Bibliographic record
Abstract
Background and purpose: In Ontario (Canada’s most populous province), it has been suggested that mildly impaired stroke patients are being admitted to inpatient rehabilitation unnecessarily due to a lack of alternative options in the community. This ecological study aimed to formally test this hypothesis. Methods: Patients admitted to an inpatient rehabilitation bed in Ontario’s most highly functioning patient classification group (Rehabilitation Patient Group 1160) were retrospectively identified as potentially avoidable admissions, and the proportion of such patients was calculated for each Local Health Integration Network every year between 2006/2007 and 2010/2011. Five indicators of community-based rehabilitation availability were used to test the relationships between avoidable admissions and resource indicators. Results: Of the 25 correlations tested, 21 agreed with the hypothesized direction of effect and 4 reached statistical significance. Logistic-linear regressions on combined data from each of the 5 years demonstrated statistically significant associations between all 5 resource indicators and the proportion of potentially avoidable admissions. Conclusions: This study confirms the suggestion of variation in the proportion of mildly impaired patients admitted to inpatient rehabilitation across Ontario’s Local Health Integration Networks. It also adds evidence to support the concern that a lack of community-based rehabilitation is contributing to these potentially avoidable admissions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".