Assessing perceptions of professionalism in medical learners by the level of training and sex
Bibliographic record
Abstract
Background: Canadian medical student and residents' severity ratings of professionalism vignettes were examined to identify the differences in ratings by the level of training and by sex. Methods: Eight hundred and thirty-five medical learners (400 medical students and 435 residents) were invited to participate in an online survey measuring medical professionalism. The survey was composed of questions about descriptive information and professionalism vignettes. The tool consists of 16 vignettes examining respondent's ability to recognize the professional and unprofessional behaviors. For each vignette, participants were asked to rate the severity of the infraction as "not a problem" to "severe." Wilcoxon rank sum tests and Fischer's Chi-square tests were used to examine the differences in perceptions of professionalism by the level of training and sex, and logistic regression models were created with the level of training and sex to examine their association with binary vignette responses (not a severe infraction and severe infraction); controlling for the effect of the other variable. Results: Overall response rate for the completed survey was 30% (n = 253). Significant differences between males and females were found for lapse in excellence (P ≤ 0.039), inappropriate dress (P ≤ 0.003), lack of altruism (P ≤ 0.033), disrespect (P ≤ 0.013), shirking duty (P ≤ 0.028), and abuse of power (P ≤ 0.006). Females rated all six vignettes as more severe as compared to males. Shirking duty (P ≤ 0.002) was found to have the differences between learner responses. Regressions found sex to be associated with severity of professionalism infractions on seven vignettes. Discussion: Future work is needed in the area of professionalism and sex to understand why female and male learners may perceive professionalism differently.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".