Analysis of factors affecting demand for rehabilitation services in Ontario, Canada: A health-policy perspective
Bibliographic record
Abstract
UNLABELLED: Demand for health services tends to outstrip supply in an environment of economic scarcity. PURPOSE: In this research, we first explore factors affecting demand for rehabilitation services in Canada's most populous province of Ontario; we then interpret these findings and discuss their implications for future demand. METHODS: Consistent with health-policy case-study methodology, we triangulated primary and secondary data sources (42 key-informant interviews and review of publicly available documents, respectively). RESULTS: Demand for rehabilitation seems to be rising quickly across Ontario's continuum of care, and informants identified four primary factors: (1) overall population growth along with an increasingly large cohort aged 65 years or older; (2) increasing rates of chronic and complex conditions, along with changes in hospital discharge patterns; (3) increasing public expectations; and (4) advances in treatment and management of diseases and condition. CONCLUSIONS: Although demand may be rising, access to rehabilitation is now based more on eligibility than on demand alone. The presence of increasing demand does not ensure that there is, or will be, sufficient financial or human resources to meet such demand. This study signals the need to reflect on current policies regarding access, and highlights the need to consider the benefits of health-promotion and injury-prevention strategies in mediating demand.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".