Wait Times for Publicly Funded Outpatient and Community Physiotherapy and Occupational Therapy Services: Implications for the Increasing Number of Persons with Chronic Conditions in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Timely access to publicly funded health services has emerged as a priority policy issue across the continuum of care from hospitals to the home and community sector. The purpose of this study was to examine wait lists and wait times for publicly funded outpatient and community occupational therapy (OT) and physical therapy (PT) services. METHODS: A mailed self-administered questionnaire was sent in December 2005 to all publicly funded sites across Ontario that deliver outpatient or community OT or PT services (N = 374). Descriptive statistics were used to describe the study sample and to examine wait lists and wait times by setting and client condition. RESULTS: Overall response rate was 57.2% (n = 214). More than 10,000 people were reported to be waiting for OT or PT services across Ontario. Of these, 16% (n = 1,664) were waiting for OT and 84% (n = 8,842) for PT. Of those waiting for OT, 59% had chronic conditions and half were waiting for home care rehabilitation services. Of those waiting for PT, 73% had chronic conditions and 81% were waiting at hospital outpatient departments. CONCLUSIONS: Individuals with chronic conditions experience excessive wait times for outpatient and community OT and PT services in Ontario, particularly if they are waiting for services in hospital outpatient departments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".