Roles of physiotherapists in primary care teams: a scoping review
Bibliographic record
Abstract
OBJECTIVES: We aimed to provide an overview of the existing knowledge regarding the roles of physiotherapists in primary care teams. DESIGN: (2010). DATA SOURCES AND STUDY SELECTION: A search strategy was carried out across the Medline, CINAHL, Academic Search Complete and AMED databases in June 2023. Selected articles, based on qualitative or mixed design studies, had to report on the roles of physiotherapists working in team-based primary care organisations and be published in the last 10 years. DATA EXTRACTION AND ANALYSIS: was used to identify all the roles undertaken by physiotherapists. RESULTS: The database search yielded 2324 articles. From the 13 included articles, 6 main themes emerged: conduct client assessment for musculoskeletal conditions, participate in health promotion and prevention, promote self-management support, communicate with patients, collaborate with other primary care providers and partners, and provide holistic care. CONCLUSIONS: The review identified a wide variety of roles, primarily related to the treatment of musculoskeletal patients. In primary care settings, interprofessional collaboration can be hindered by a lack of knowledge regarding the roles of physiotherapists. Future studies should aim to develop effective strategies to ensure that all primary care team members have a comprehensive understanding of the roles of physiotherapists and to explore roles associated with non-traditional forms of physiotherapy practice.
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 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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".