Prescription medication by physiotherapists: a Brazilian view of the United Kingdom, Canada, Australia and New Zealand
Bibliographic record
Abstract
Many health systems (HS) have adopted novel models of care which have included non-medical prescription (NMP) by physiotherapists. The aim of this study was to verify in the literature the existence of this practice and its possible benefits. A literature review was carried out through search on Science Direct, PubMed, SciELO, Lilacs and Google Scholar, and in the World Confederation for Physical Therapy and Chartered Society of Physiotherapy websites. In recent decades the United Kingdom adopted the NMP for health professionals, followed by Canada. In Australia and New Zealand physiotherapists have acted in the prescription and administration of medications under medical orders, which is the first step into independent prescription. Brazilian physiotherapists cannot prescribe any medication, despite of high demands from patients in the Brazilian HS, shortage of physicians in many regions and bureaucracy in accessing health services. The adoption of NMP by physiotherapists may play an important role in the HS, and it seems to be an inevitable achievement in the next years in Australia and New Zealand. The main benefits include decreasing bureaucracy for assistance, population demands for medication as well as major professional refinement.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".