Key differences between severity of disciplinary issues and medical student insights
Bibliographic record
Abstract
CONTEXT: This study explores the reliability of tools designed to rate the type of remediable medical student offences, their severity, and the quality of student insight in response to the remediation, and tests the relationships between these three constructs. METHODS: Data were collected via retrospective appraisal of remediation files from the 2009-2016 incoming classes of McMaster University's medical programme. Across two studies, 12 faculty members categorised the offences by type (academic or professionalism), and rated severity and insight by way of single anchored Likert scales. In Study 1, Krippendorff's alpha and independent, two-way, consistency type, average measures (k = 6), random-effects inter-rater reliability analyses were conducted to assess the inter-rater reliability of ratings of the measures. In Study 2, independent samples t-tests were conducted for the severity and insight measures as a function of offence type. Pearson correlations were used to assess the relationship between severity and insight as a function of offence type. RESULTS: High inter-rater reliability was found with respect to the type of offence (α = 0.86), severity (0.92) and student insight (0.88). Mean (±standard deviation) ratings of severity are significantly higher for professionalism (4.37 ± 1.20) than academic offences (2.89 ± 1.25), t(73) = -5.3, p < 0.001, |d| = 1.21, whereas the opposite is true for ratings of insight, (professionalism, 3.19 ± 1.37; academic, 4.48 ± 1.01), t(73) = 4.6, p < 0.001, |d| = 1.07. Ratings of severity and insight are moderately negatively correlated for both academic (r = -0.64, p < 0.001, n = 38) and professionalism offences (r = -0.57, p < 0.001, n = 37). CONCLUSIONS: Professionalism offences are perceived as more severe and are associated with lower insight than academic offences, pointing to the difficulty that learners face in assessing the constitution of a professionalism offence. This illustrates a need for deeper consideration about remedial strategies for lapses in professionalism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".