Bibliographic record
Abstract
AbstractIntroduction: Harassment of medical trainees is a widely documented and pervasive problem that needs to be addressed by higher education institutions and governing bodies. Canadian universities are required to provide harassment policies to students and employees. However, under-reporting of harassment is still a major area of concern. At this time, there is no standardized criteria employed to ensure that all Canadian medical university policies are comprehensive, accessible, and clear. Chapter 1 of this thesis provides an overview of the harassment definitions and the current prevalence of medical trainee harassment documented across Canadian and top international medical universities. In Chapter 2, I introduce the barriers to reporting harassment and the issue of under-reporting and present an organizational framework, and ways that this tool can be applied to inform harassment policy development and also introduce the policy evaluation manuscript. In Chapter 3 I will discuss the organizational structure and culture of medical training. In Chapter 4, I outline the objectives and hypothesis for this thesis. Chapter 5 includes the policy evaluation manuscript. Here, I will discuss a set of adapted and extended criteria used to assess the comprehensiveness of the 17 Canadian medical universities’ and the top 10 QS-ranked universities’ harassment policies.Methods: In chapter 5, I adapted a policy evaluation criteria to evaluate the harassment policies of the 17 Canadian medical universities and the top 10 QS-ranked universities. A total of 35 Canadian and 16 top 10 QS-ranked universities’ workplace and university harassment policies were evaluated, scored, and analyzed for strengths and weaknesses based on the adapted and extended criteria.Results: Our evaluation show areas of strength for Canadian universities, such as distinct harassment definitions and mentioning of harassment training for staff and students, and room for improvements such as a lack of detail in the informal complaint procedures and few policies mentioning the availability of an ombudsperson or student representative. This adapted criterion can be used for future policy assessment and development across Canadian medical universities. Conclusions: Chapter 6 discusses conclusions, areas for improvement among Canadian university harassment policies and discusses future directions for this research, such as the application of this criteria in future policy development.Keywords: Harassment, Canadian medical university harassment policies, medical trainee
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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 | 0.133 | 0.208 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.010 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".