An initiative to improve equity, timeliness and access to District Health Board-funded physiotherapy in Canterbury, Christchurch, New Zealand
Bibliographic record
Abstract
Background and context General practice teams frequently request orthopaedic and musculoskeletal physiotherapy. In the Canterbury District Health Board (DHB) region, before November 2018, the criteria for DHB-funded physiotherapy were unclear. Wait times were many months. Care was provided on hospital sites. Limited data were available about the service. Assessment of problem A clinical project group including private and DHB hospital physiotherapists and general practitioners was established. Patients requiring orthopaedic and musculoskeletal physiotherapy who had certain criteria were seen by physiotherapists in contracted private clinics in the community instead of by physiotherapists in hospital departments. Patients received up to NZ$300 (excluding GST) of care. A claiming process was established that required the physiotherapy clinics to provide data on patient outcomes. Results In the first 12 months of the programme, 1229 requests were accepted. Patients waited an average of 11.1 days for their first appointment. There was an average Patient Specific Functional Scale increase of 3.7 after treatment. Strategies for improvement A change environment was critical for this community-based, geographically distributed model to succeed. It was supported by key clinicians and funders with sufficient authority to make changes as required. It required ongoing clinical oversight and operational support. Lessons DHB orthopaedic and musculoskeletal physiotherapy can be moved from hospital sites to a community-based, distributed service in a timely, effective and equitable fashion. There was a prompt time to treatment. Data collection was improved by tracking 'before' and 'after' measures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".