Did waiting times really decrease following a service reorganization? Results from a retrospective study in a pediatric rehabilitation program in Québec
Bibliographic record
Abstract
Purpose: To examine changes in waiting times and types of services received before, during and after a pediatric rehabilitation service reorganization including new admission procedures; To compare waiting time data available in the program’s administrative database and children’s medical files. Method: Waiting time was defined as the time elapsed between referral and accessing a service provided by any clinician in the program (program waiting time) or by any clinician within a discipline (discipline-specific waiting time). Services were categorized as individual, group treatment, or other. ANOVAs and χ2 tests were used to examine changes in waiting times and type of services, respectively. Paired T-tests compared the program waiting times from the two databases. Results: Data were collected on 188 children (mean age: 4 years and 1 month). The program and occupational therapy waiting times were shorter following the service reorganization. For two disciplines, the proportion of children receiving individual treatment diminished over time, while group and other types of interventions increased. Program waiting times calculated using the two data sources did not differ significantly but differences in the available data highlighted administrative issues. Conclusions: Service reorganization can decrease waiting times and change the type of services offered over time.Implications for RehabilitationService reorganization can improve accessibility by reducing the first waiting times.More researches are needed to understand the impact of the changes in the type of services provided on service accessibility and service quality.Service accessibility should be monitored using accurate data extending beyond those routinely collected.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".