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Record W2593992315 · doi:10.3138/ptc.2016-22

To Be or Not to Be a Cardiorespiratory Physiotherapist: Factors That Influence Career Choice in a Sample of Canadian Physiotherapists

2017· article· en· W2593992315 on OpenAlexaffvenueabout
Laura Hussey, Danijel Sredic, Colby Bucci, Ian R. Barrett, Ryan McLeod, Tania Janaudis‐Ferreira, Dina Brooks

Bibliographic record

VenuePhysiotherapy Canada · 2017
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoMcGill University
Fundersnot available
KeywordsMentorshipCardiorespiratory fitnessScope (computer science)MedicinePhysical therapyScope of practiceClinical PracticeSample (material)Perspective (graphical)Medical education

Abstract

fetched live from OpenAlex

Purpose: This study explored the factors that influence choosing or not choosing a career in cardiorespiratory physiotherapy (CRP) from the perspective of a group of currently practising, experienced physiotherapists in Canada. Methods: A modified Dillman approach was used to distribute a cross-sectional, self-administered, online questionnaire to all eligible members of the cardiorespiratory and orthopaedic divisions of the Canadian Physiotherapy Association. A total of 438 participants—21 CRP and 417 non-CRP therapists—completed the survey. The survey response rate was 9.4%. Results: A narrow scope of practice (61.9%) and a lack of interest in CRP subject matter (50.1%) were the most influential factors deterring the respondents from making CRP their career choice. Interest in CRP (81.0%), mentorship (76.2%), access to physical resources (76.2%), and inter-professional practice (71.4%) were the most influential factors in pursuing a career in CRP. Conclusion: Increasing the awareness of the scope of practice for CRP, exposure to positive mentors, and rich practice settings are key factors in promoting physiotherapists' specialisation in CRP.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.332
Teacher spread0.285 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2017
Admission routes3
Has abstractyes

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