Delisting publicly funded community-based physical therapy services in Ontario, Canada: A 12-month follow-up study of the perceptions of clients and providers
Bibliographic record
Abstract
Publicly funded community-based physical therapy (PT) services in Canada's most populous province of Ontario were partially delisted, or deinsured, in April 2005. Two previous studies examined the short-term effects from the client and provider perspectives; and in this study, we follow up with participants from these preceding studies to assess long-term consequences of this policy. Sixteen of 18 providers (89%) and 64 of 98 clients (65%) agreed to participate in a follow-up telephone interview. Our results indicate that 12 months following delisting, and despite government assurances that access would be preserved, clients rendered ineligible for publicly funded services report ongoing access barriers across Ontario. Clients in this study also express concern about their overall health and report an increased use of other insured health professionals (e.g., physicians) and services (e.g., hospitals). On the other hand, providers within the network of publicly funded clinics report an important decrease in demand for PT services, whereas those from other settings report little change. We conclude that delisting policies may have long-term consequences on uninsured or underinsured clients and that evidence-based policy planning is warranted to ensure that the goals of reform are aligned with the desired outcomes at the client, provider, and system levels.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".