MétaCan
Menu
Back to cohort

Before the white coat: perceptions of professional lapses in the pre‐clerkship

2004· article· en· W2042448918 on OpenAlexaff
Shiphra Ginsburg, Natasha Kachan, Lorelei Lingard

Bibliographic record

VenueMedical Education · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBlameAccountabilityEntitlement (fair division)Medical educationPsychologyClinical clerkshipResistance (ecology)PerceptionMedicinePedagogySocial psychologyCurriculum

Abstract

fetched live from OpenAlex

BACKGROUND: It has been shown that the professional development of clinical clerks is influenced by their experiences of unprofessional behaviour, but the perceptions of pre-clerkship students have received relatively little attention. Our purpose was to develop a greater contextual understanding of the situations in which pre-clerkship students encounter professional challenges, and to investigate what pre-clerkship students consider to be professional lapses in these situations. METHODS: We conducted 4 focus groups (n = 22 students); transcripts were analysed by 3 researchers using grounded theory. RESULTS: Pre-clerkship students reported lapses in the areas of communicative violation, role resistance, objectification, accountability and harm, validating our previous clerkship-based framework. However, they also reported numerous lapses committed by fellow students and many instances of lack of accountability to students, which were not reported by clerks. Many of their reports involved non-health care professionals. CONCLUSIONS: The willingness of pre-clerkship students to report on fellow students was associated with a tendency to blame their colleagues, at the expense of a more reflective analysis, and their views on professionalism appeared to be generic rather than medicine-specific. We should reinforce students' appreciation of these generic values and add on medicine-specific values as the students progress, in order to better cultivate professionalism without entitlement.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0010.000

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 head0.011
GPT teacher head0.368
Teacher spread0.357 · 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

Citations34
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueMedical EducationSame topicInnovations in Medical EducationFrench-language works237,207