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Record W4308995573 · doi:10.3138/ptc-2022-0027

Difference in Physiotherapy Students’ Training and Objective Structured Clinical Exam Performance Before and During COVID-19

2022· article· en· W4308995573 on OpenAlexaffvenue
Gregory F. Spadoni, Sarah Wojkowski, Paul W. Stratford

Bibliographic record

VenuePhysiotherapy Canada · 2022
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Physical therapyMedical education2019-20 coronavirus outbreakTraining (meteorology)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Physical medicine and rehabilitationMedicinePsychologyComputer science

Abstract

fetched live from OpenAlex

Purpose: The Objective Structured Clinical Examination (OSCE) and station examinations, in general, have been widely utilized in health professional programmes to evaluate students' clinical performance prior to advancing to a clinical placement. The COVID-19 pandemic impacted student preparation and implementation of our programme's OSCEs. The impact on changes in student OSCE performance due COVID-19 has not been well studied. This non-concurrent cohort study evaluated the difference before and during COVID-19 pandemic on Year 1 physiotherapy students' performances on an in-person OSCE by estimating the mean difference in cohort OSCE scores and safety occurrences. Methods: Two cohorts of MSc (PT) students were compared: Cohort A (not impacted by COVID-19) and Cohort B (impacted by COVID-19). Cohort scores were summarized as means and 95% CIs. Results: = 4.0, 95% CI: 2.1, 5.8). Cohort B students were approximately 4 times more likely to demonstrate safety occurrences. Conclusion: The impact of COVID-19 did not adversely affect total OSCE scores; however, it did increase safety infractions.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.387
Teacher spread0.358 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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