Access to publicly funded outpatient physiotherapy services in Quebec: waiting lists and management strategies
Bibliographic record
Abstract
PURPOSE: Problems with access to outpatient physiotherapy services have been reported in publicly funded healthcare systems worldwide. A few studies have reported management strategies aimed at reducing extensive waiting lists, but their association with waiting times is not fully understood. The purpose of this study was to document access to public outpatient physiotherapy services for persons with musculoskeletal disorders in hospitals and explore organizational factors associated with waiting time. METHODS: We surveyed outpatient physiotherapy services in publicly funded hospitals in the province of Quebec (Canada). RESULTS: A total of 97 sites responded (99%) to the survey. The median waiting time was more than six months for 41% of outpatient physiotherapy services. The waiting time management strategies most frequently used were attendance and cancelation policies (99.0%) and referral prioritization (95.9%). Based on multivariate analyses, the use of a prioritization process with an initial evaluation and intervention was associated with shorter waiting times (p = 0.008). CONCLUSIONS: Our findings provide evidence that a large number of persons wait a long time for publicly funded physiotherapy services in Quebec. Based on our results, implementation of a prioritization process with an initial evaluation and intervention could help improve timely access to outpatient physiotherapy services. Implications for Rehabilitation Access to publicly funded outpatient physiotherapy services is limited by long waiting times in a great proportion of Quebec's hospitals. The use of a specific prioritization process that combines an evaluation and an intervention could possibly help improve timely access to services. Policy-makers, managers, and other stakeholders should work together to address the issue of limited access to publicly funded outpatient physiotherapy services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".