Prioritization of Referrals in Outpatient Physiotherapy Departments in Québec and Implications for Equity in Access
Bibliographic record
Abstract
In the context of long waiting time to access rehabilitation services, a large majority of settings use referral prioritization to help manage waiting lists. Prioritization practices vary greatly between settings and there is little consensus on how best to prioritize referrals. This paper describes the prioritization processes for physiotherapy services in Québec and its potential implications in terms of equity in access to services. This is a secondary analysis of a survey of outpatient physiotherapy departments (n=98; proportion of participation was 99%) conducted in 2015 across publicly funded hospitals in Québec. In many settings, persons with acute orthopaedic conditions were prioritized while chronic conditions were given a lower priority. There were 72 different combinations of prioritization criteria used in outpatient physiotherapy departments. Variability was also observed in the type of personnel involved in the prioritization process, the number of priority levels used to rank the referrals and the source of information used to prioritize referrals. These results highlight potential issues regarding equity in access to physiotherapy services: the prioritization of persons with acute conditions to the detriment of those with chronic conditions, the lack of consensus on a fair prioritization process and the importance to adequately assess patients’ needs for treatment. Further research and interventions on prioritization criteria and processes are needed to ensure equitable access to physiotherapy services, especially in the public sector.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".