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Record W4387106810 · doi:10.3138/ptc-2023-0030

Pelvic Health Content in Canadian Entry-To-Practice Physiotherapy Programs: An Online Survey

2023· article· en· W4387106810 on OpenAlexaffvenueabout
Stephanie Scodras, Euson Yeung, Heather Colquhoun, Susan Jaglal, Nancy M. Salbach

Bibliographic record

VenuePhysiotherapy Canada · 2023
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsMedicinePelvic floorPhysical therapyCurriculumPelvic examinationPelvic painGynecologyPsychologySurgeryPedagogy

Abstract

fetched live from OpenAlex

Purpose: Pelvic health physiotherapy is an emerging and sensitive area of practice that offers effective conservative treatment for pelvic health conditions. Canadian entry-to-practice curriculum guidelines accord programs considerable flexibility regarding incorporating pelvic health content, which may lead to differences between programs and diverse levels of competence among new graduates. The purpose of this study was to determine the nature and extent to which pelvic health content is incorporated in entry-to-practice physiotherapy programs in Canada. Method: We conducted a descriptive cross-sectional e-survey of representatives from Canadian entry-to-practice physiotherapy programs. Results: = 6). All participating programs covered musculoskeletal-related conditions, urinary incontinence, and pelvic pain conditions, and included anatomy and physiology, clinical reasoning, subjective assessment and pelvic floor muscle training topics. Three programs trained students in internal pelvic floor techniques in elective courses. All programs covered cisgender women populations, however, transgender populations were seldom covered. Conclusions: This study provides an understanding of pelvic health curricular content that can serve as a first step towards standardizing and improving entry-level pelvic health training in Canada.

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 score0.345
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
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.369
Teacher spread0.302 · 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

Citations7
Published2023
Admission routes3
Has abstractyes

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