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Record W4367668633 · doi:10.1080/09593985.2023.2206482

Physical therapy students’ application of an imaging decision rule for acute knee pain

2023· article· en· W4367668633 on OpenAlexaboutno aff
B. James Massey, Jason Grandeo, Laura Favaro, Rebecca Bliss, Kendra Gagnon, Jodi L. Young

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkMedicinePhysical therapyLikert scaleReferralPhysical therapy educationRespondentSelf-efficacyKnee painPhysical examFamily medicinePhysical therapistMedical educationPsychologyAlternative medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Background Evidence supports direct referral for imaging by physical therapists. Accuracy and self-efficacy for imaging decisions have not been investigated in entry-level doctor of physical therapy (DPT) students.Objective The purpose of this study was to understand the relationship between entry-level DPT instruction and accuracy and self-efficacy for imaging referral due to acute knee trauma. A second purpose was to identify relationships between accuracy and self-efficacy.Methods An online survey was sent via e-mail to program directors in accredited DPT programs in the United States with an invitation to forward the survey to DPT students. The survey captured demographic information and included five questions that assessed the respondent’s ability to apply the Ottawa Knee Rules (OKR). Self-efficacy was assessed using the Physiotherapist Student Self-Efficacy (PSE) questionnaire, a self-rated 5-point Likert scaled tool.Results Of 240 surveys, DPT students who completed imaging coursework had greater accuracy and higher self-efficacy (68.0% correct (95% CI, 63.6–72.5), PSE = 3.67, P < .001) compared to students who had not (45.8% correct (95% CI, 40.8–50.7), PSE = 2.67, P < .001). Conclusion: Accuracy by DPT students who completed imaging coursework was significantly improved and comparable to values from autonomous providers.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.447
Teacher spread0.433 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2023
Admission routes1
Has abstractyes

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