Improving Administrative Outcomes in Physiotherapy by Adopting Open-Access Booking
Bibliographic record
Abstract
Purpose: Long wait times for physiotherapy are associated with poorer health trajectories for clients. Clients’ experiences with physiotherapy services in Saint John were suboptimal; thus, this study explored making administrative changes to improve those experiences. All physiotherapy services adopted an administrative model called open-access booking (OAB), which blended elements of advanced access, triage, and centralized wait lists. Method: OAB was instituted in the first week of February 2017 and has been active since. The researcher accessed more than 20,000 anonymized case records spanning 5 years (February 2014–January 2019) and compared the 3-year pre-OAB phase with the 2-year OAB phase using interrupted time series analysis models. Results: OAB appeared to not be associated with changes in client volume, but it was associated with fewer “on-paper” clients, shorter wait times to first appointment, more consistent record keeping, a greater likelihood of being discharged after one appointment, and fewer appointments before discharge. There was less variability in these outcomes after the adoption of OAB, suggesting a more stable client experience with the physiotherapy system. Conclusions: OAB appears to be associated with improved administrative outcomes, but strict causality cannot be assessed. The results are promising but not conclusive.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".