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Record W4386252481 · doi:10.1002/msc.1813

Core competencies for first contact physiotherapists in a direct access model of care for adults with musculoskeletal disorders: A scoping review

2023· review· en· W4386252481 on OpenAlexaff
Robin Vervaeke, Simon Lafrance, Anthony Demont

Bibliographic record

VenueMusculoskeletal Care · 2023
Typereview
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineCore competencyThematic analysisPsychological interventionNursingIntervention (counseling)Core (optical fiber)Physical therapyQualitative research

Abstract

fetched live from OpenAlex

INTRODUCTION: To optimise the management of Musculoskeletal disorders (MSKDs), many countries have implemented direct access to physiotherapy; however, the core competencies required for first contact physiotherapists (PTs) have not been precisely defined. The aim of this scoping review is to identify and describe the core competencies required for first contact PTs treating adults with MSKDs. METHODS: We conducted a scoping review of the literature by searching eight databases and grey literature up to July 2023. We performed a thematic analysis of the competencies identified based on predefined themes relevant to first contact physiotherapy in direct access models in primary or emergency care settings. RESULTS: Sixty-five articles were included. Seventeen core competencies were identified and grouped into 5 themes: (1) Assessment and examination; (2) Management and interventions; (3) Communication; (4) Cooperation and collaboration; and (5) Professionalism and leadership. CONCLUSIONS: Our findings provide an international perspective on the core competencies required for first contact PTs.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0100.007
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.212
GPT teacher head0.540
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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