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Record W4391878479 · doi:10.1097/phm.0000000000002455

Evidence-based practice education in physical medicine and rehabilitation residency programs: A Canadian national survey

2024· article· en· W4391878479 on OpenAlexaffabout
Dinesh Kumbhare, John‐Ross Rizzo, Allison Bean, Thiru M. Annaswamy

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersU.S. Department of Veterans Affairs
KeywordsJournal clubMedicineCurriculumMedical educationCohortMEDLINEResidency trainingFamily medicineEvidence-based practiceRehabilitationEvidence-based medicineTraining (meteorology)Clinical PracticeAlternative medicinePhysical therapyPsychologyContinuing educationPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: The current extent and quality of evidence based practice training for physiatrists is unclear at this time. Training of evidence-based practice is also available to residents in Canada. The extent, quality, and impact of the training were explored. DESIGN: This is a cohort study. RESULTS: About half of the Canadian programs reported a formal evidence-based practice curriculum. The most frequently reported method of providing evidence-based practice education was resident participation in journal club. CONCLUSIONS: Despite the increasing integration of evidence-based practice into residency program education, there remains a critical lack of knowledge and skills for implementation of evidence-based practice into clinical practice among Canadian physical medicine and rehabilitation residency programs.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.112
GPT teacher head0.541
Teacher spread0.429 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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