In Which Scenarios Do Preceptors and Students Agree, Disagree, or Remain Neutral About Learner Mistreatment?
Bibliographic record
Abstract
Objective: In recent years, nearly half of graduating medical students in the USA and Canada reported personal mistreatment experiences during training. Prior scholarship reports heterogeneous opinions of learner mistreatment behaviors among trainees, and resulting unaligned perceptions may influence reporting, feedback, and policy. However, fewer studies compare students' and preceptors' views about learner mistreatment using vignettes of student-preceptor interactions. Methods: We surveyed 141 students and 203 preceptors at an MD-granting institution. Participants indicated their agreement on a 5-point scale on whether behaviors in 17 written vignettes constituted learner mistreatment. Descriptive statistics and bivariate tests were executed to identify areas in which students' and preceptors' mistreatment views differed. Results: Student and preceptor responses converged on 12 of 17 vignettes. More students agreed that vignettes describing a student being addressed by another student's name and an example of public embarrassment constitutes learner mistreatment. More preceptors agreed that addressing students with terms of endearment and denying training opportunities based on career choice constitute learner mistreatment. The proportion of preceptors who selected neutral responses was higher than that of students for nearly all vignettes. Conclusion: When presented with written vignettes, students' and preceptors' perceptions of learner mistreatment were not consistent. This study highlights potential gaps in students' and preceptors' mistreatment perceptions, including differences in participants' decisions to remain "neutral." To prevent and address learner mistreatment, follow-up research is warranted to support the development and honing of shared definitions and examples of mistreatment, as well as targeted programming for students and preceptors.
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 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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".