Supplemental Material for Anti-harassment Bystander Educational Intervention Study 1
Bibliographic record
Abstract
Compiled Supplemental Material for Anti-harassment Bystander Educational Intervention Study 1 Jason M. Harley*; Ph.D., Associate Professor; Department of Surgery, McGill University; Institute for Health Sciences Education, McGill University; Research Institute of the McGill University Health Centre; Steinberg Centre for Simulation and Interactive Learning, McGill University Byunghoon “Tony” Ahn; Ph.D., Department of Surgery, McGill University *Corresponding Author: Jason.harley@mcgill.ca Our Supplemental Materials, also referred to as Supplementary Digital Content, include: Supplemental Material A/1: The consent form given to participants Supplemental Material B/2: A description of how we incorporated psychological safety into our anti-harassment educational intervention. Supplemental Material C/3: Simulation Scenario Progression Outline Supplementary Material D/4: Pre-briefing Guideline for Simulation Facilitators Supplementary Material E/5: Debriefing Guideline for Simulation Facilitators A version of the supplemental material was published under a Creative Commons license in the supplemental material in the following journal article for Study 2 of the larger program of research: Johnson, M., Ahn, B.T., Grewal, K., Matin, N., Pelletier, J-S., Feldman, L.S., Fried, G.M., & Harley, J.M. Get Over It: Surgical Residents’ Responses to Simulated Harassment. A Multi-Method Study (2025). Journal of Surgical Education, 82(3), 1-10. DOI.10.1016/j.jsurg.2024.103397 This version of the file includes additional details in the Supplemental Material C/3: Simulation Scenario Progression Outline along with some grammatical edits. Funded by: Social Sciences and Humanities Research Council of Canada 430-2020-00573 awarded to PI, Jason M. Harley IRB Study Number A07-B81-22B IRB Study Name: Understanding medical trainees’ knowledge and attitudes towards harassment
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.148 | 0.015 |
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; both teacher heads agree on what is shown here.
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".