Allocation of Rehabilitation Services for Older Adults in the Ontario Home Care System
Bibliographic record
Abstract
Background: Physiotherapy and occupational therapy services can play a critical role in maintaining or improving the physical functioning, quality of life, and overall independence of older home care clients. Despite their importance, however, there is limited understanding of the factors that influence how rehabilitation services are allocated to older home care clients. The aim of this pilot study was to develop a preliminary understanding of the factors that influence decisions to allocate rehabilitation therapy services to older clients in the Ontario home care system, as perceived by three stakeholder groups. Methods: Semi-structured interviews were conducted with 10 key informants from three stakeholder groups: case managers, service providers, and health system policymakers. Results: Drivers of the allocation of occupational therapy and physiotherapy for older adults included functional needs and postoperative care. Participants identified challenges in providing home care rehabilitation to older adults, including impaired cognition and limited capacity in the home care system. Conclusions: Considering the changing demands for home care services, knowledge of current practices across the home care system can inform efforts to optimize rehabilitation services for the growing number of older adults. Further research is needed to advance the understanding of, and optimize rehabilitation service allocation to, older frail clients with multiple morbidities. Developing novel decision-support mechanisms and standardized clinical care pathways for older client populations may be beneficial.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".