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Record W2162316517 · doi:10.1186/1472-6920-13-69

Teaching and assessing procedural skills: a qualitative study

2013· article· en· W2162316517 on OpenAlexaff
Claire Touchie, Susan Humphrey‐Murto, Lara Varpio

Bibliographic record

VenueBMC Medical Education · 2013
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of OttawaOttawa Hospital
Fundersnot available
KeywordsMedical educationQualitative researchPsychologyMedicineMathematics educationSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Graduating Internal Medicine residents must possess sufficient skills to perform a variety of medical procedures. Little is known about resident experiences of acquiring procedural skills proficiency, of practicing these techniques, or of being assessed on their proficiency. The purpose of this study was to qualitatively investigate resident 1) experiences of the acquisition of procedural skills and 2) perceptions of procedural skills assessment methods available to them. METHODS: Focus groups were conducted in the weeks following an assessment of procedural skills incorporated into an objective structured clinical examination (OSCE). Using fundamental qualitative description, emergent themes were identified and analyzed. RESULTS: Residents perceived procedural skills assessment on the OSCE as a useful formative tool for direct observation and immediate feedback. This positive reaction was regularly expressed in conjunction with a frustration with available assessment systems. Participants reported that proficiency was acquired through resident directed learning with no formal mechanism to ensure acquisition or maintenance of skills. CONCLUSIONS: The acquisition and assessment of procedural skills in Internal Medicine programs should move toward a more structured system of teaching, deliberate practice and objective assessment. We propose that directed, self-guided learning might meet these needs.

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)0.002
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.022
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)0.0010.000

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 head0.026
GPT teacher head0.457
Teacher spread0.431 · 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 designQualitative
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

Citations33
Published2013
Admission routes1
Has abstractyes

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