When the Learning Environment Is Suboptimal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Despite widespread implementation of policies to address mistreatment, high rates of mistreatment during clinical training are reported, prompting the question of whether "mistreatment" means more to students than delineated in official codes of conduct. Understanding "mistreatment" from students' perspective and as it relates to the learning environment is needed before effective interventions can be implemented. METHOD: The authors conducted focus groups with final-year medical students at McGill University Faculty of Medicine in 2012. Participants were asked to characterize "suboptimal learning experience" and "mistreatment." Transcripts were analyzed via inductive thematic analysis. RESULTS: Forty-one of 174 eligible students participated in six focus groups. Students described "mistreatment" as lack of respect or attack directed toward the person, and "suboptimal learning experience" as that which compromised their learning. Differing perceptions emerged as students debated whether "mistreatment" can be applied to negative learning environments as well as isolated incidents of mistreatment even though some experiences fell outside of the "official" label as per institutional policies. Whether students perceived "mistreatment" versus a "suboptimal learning experience" in negative environments appeared to be influenced by several key factors. A concept map integrating these ideas is presented. CONCLUSIONS: How students perceived negative situations during training appears to be a complex process. When medical students say "mistreatment," they may be referring to a spectrum, with incident-based mistreatment on one end and learning-environment-based mistreatment on the other. Multiple factors influenced how students perceived an environment-based negative situation and may provide strategies to improving the learning environment.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it