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The effectiveness of unannounced standardised patients in the clinical setting as a teaching intervention

2004· article· en· W1985429737 on OpenAlexafffund
Debbie Elman, Rosalie Hooks, Diana Tabak, Glenn Regehr, Risa Freeman

Bibliographic record

VenueMedical Education · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersUniversity of Toronto
KeywordsIntervention (counseling)PreceptorBiopsychosocial modelPresentation (obstetrics)PsychologyMedicineObjective structured clinical examinationFamily medicineMedical educationClinical psychologyPsychiatrySurgery

Abstract

fetched live from OpenAlex

PURPOSE: Teaching medical students to spontaneously identify biopsychosocial issues (e.g. family violence) remains a challenge. We examined the extent to which using unannounced standardised patients (SPs) presenting in a clerk's clinical setting could assist with this teaching challenge. METHODS: All clerks attended a family violence seminar in their family medicine rotation. Intervention students additionally saw an unannounced SP portraying 1 of 2 scenarios in their preceptor's office during the rotation, and received immediate feedback about their performance. An end of rotation objective structured clinical examination (OSCE) included an SP presentation similar to that seen by the intervention students. RESULTS: Clerks who received the intervention demonstrated increased questioning about family violence, from 0% (0 of 29 students) to 19% (5 of 26 students) in 1 OSCE scenario (P = 0.019), and from 40% (12 of 30 students) to 76% (19 of 25 students) in the other (P = 0.007). CONCLUSIONS: Seeing unannounced SPs had a dramatic effect on later student performance. This potentially powerful intervention could be applied to a range of clinical issues.

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)0.015
metaresearch head score (Gemma)0.051
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.051
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 integrity0.0000.001
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 head0.009
GPT teacher head0.418
Teacher spread0.409 · 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

Citations22
Published2004
Admission routes2
Has abstractyes

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