It takes (at least) two to tango: comparing the affordances of two learner mistreatment reporting mechanisms
Bibliographic record
Abstract
Background: The affordances, or characteristics, of learner mistreatment reporting mechanisms can shape the information elicited from these mechanisms, which has an impact on how institutions understand the scope and nature of learner mistreatment. This study compares whether and how the affordances of two mistreatment reporting mechanisms elicit different information about learner mistreatment at a single institution. Methods: We conducted an interpretive content analysis of reports submitted using two mechanisms, one that elicits reports through end-of-rotation evaluations and one through a voluntary web-based system, between July 2015 and December 2021. We extracted the metadata from reports and applied a coding framework informed by the Healthcare Complaints Analysis Tool (HCAT) to the narrative descriptions in reports. Data analysis included descriptive statistics and the chi-square test on SPSS v.27. Results: We collected 90 elicited reports and 240 voluntary reports of mistreatment. Similar types of mistreatment were reported through each mechanism, but disrespectful behaviour and bias and discrimination were reported significantly more in voluntary reports. Elicited reports most frequently included incidents of learner mistreatment in clinical settings, whereas voluntary reports included incidents from a wide variety of settings and people or were issues other than mistreatment. Discussion: Utilizing multiple learner mistreatment reporting mechanisms with different affordances can mitigate the limitations of a single mechanism, help identify a more nuanced understanding of learner mistreatment, and increase reporters' choices for how and when to report mistreatment. Increased information allows an institution to address specific incidents and develop targeted, preventive educational activities and faculty development.
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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 | 0.091 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".