Integrating Physiotherapy into Primary Care Models: A Scoping Review Protocol
Bibliographic record
Abstract
Abstract Introduction Physiotherapists (PTs) working in primary care settings within an interprofessional team can lead to favourable health outcomes and decreased burden on the healthcare system. Although PT models of care are important to primary care delivery, there is a lack of knowledge and evidence on the characteristics of these models of care, the differences and similarities between the models, and the barriers and facilitators to implementing these models. This scoping review protocol aims to fill this knowledge gap by synthesizing the evidence and characteristics of models of care that integrate physiotherapists within primary care teams, mapping the similarities and differences, and describing barriers and facilitators to implementing models of care that integrate physiotherapists within primary care teams. Methods The scoping review is based on the Joanne Briggs Institute (JBI) framework. It is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRIMSA-ScR). A comprehensive search strategy will be used to find relevant papers in six databases: OVID MEDLINE, PubMed, Embase, CINAHL, Web of Science, and Scopus. Grey literature will be searched through OpenGrey, Theses Global, ProQuest Dissertation, and Google Scholar. Quantitative and qualitative study designs will be included, with two reviewers independently selecting each article on Covidence. Data will be extracted using a pre-piloted data extraction sheet and synthesized narratively to identify themes and patterns. Discussion This scoping review will synthesize the evidence on models of care that integrate physiotherapists within primary care teams. It will provide evidence to inform the implementation of these models of care and identify research gaps that need to be addressed.
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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 | 0.160 | 0.130 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.016 |
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".