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Record W2950327548 · doi:10.3138/ptc-2018-0081

Clinical Instructors’ Experiences Working with and Assessing Students Who Perform below Expectations in Physical Therapy Clinical Internships

2019· article· en· W2950327548 on OpenAlexaffvenueabout
Olivia W. So, Rachael Shaw, Liam O’Rourke, Jacob T. Woldegabriel, Brittany Wade, Martine Quesnel, Brenda Mori

Bibliographic record

VenuePhysiotherapy Canada · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInternshipCurriculumThematic analysisInfluencer marketingMedical educationPsychologyClinical PracticeQualitative researchProcess (computing)MedicinePedagogyNursingComputer scienceSociologyManagement

Abstract

fetched live from OpenAlex

Purpose: Clinical education is an integral component of the curriculum of all physical therapy (PT) entry-to-practice programmes in Canada. The literature indicates that working with and assessing students performing below expectations (SPBE) can be procedurally and emotionally difficult. Our study aimed to explore the experiences of clinical instructors (CIs) and the decision-making process involved when supervising SPBE in PT. Method: A total of 19 in-depth, semi-structured interviews were conducted with CIs, transcribed, and coded using qualitative thematic analysis. Results: Four factors appeared to be important for CIs when they were deciding how to assess SPBE: (1) features of student performance, (2) factors related to the CIs, (3) academic and clinical facility influencers, and (4) strategies and available resources. Concerns about safety and professional behaviour, a student’s clinical reasoning skills, and a lack of progression were key factors that CIs considered in recommending a final grade. CIs were more likely to recommend a failing grade if there was a series of repeated incidents rather than an isolated incident. Conclusions: We make several recommendations for the student, CI, and facilities to consider to better support and facilitate the process of working with SPBE in PT clinical education.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.435
Teacher spread0.387 · 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 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

Citations6
Published2019
Admission routes3
Has abstractyes

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