An Evaluation of Cancer’s Margins Training Videos and their Impact on Medical Students’ Self-Rated Confidence in Working with Lesbian, Bisexual, and Transgender (LBT) Patients with Breast and Gynecological Cancers
Bibliographic record
Abstract
Lesbian, bisexual, and transgender (LBT) patients with breast and gynecological cancers face unique challenges and barriers to accessing LBT affirming healthcare. Physician attitudes and knowledge for working with LBT patients contribute to these challenges and barriers. Despite this, there is very limited LBT specific education in the medical curriculum. Cancer’s Margins (cancersmargins.ca) is a national project funded by the Canadian Institutes of Health Research (CIHR) that has developed a series of first voice videos which can serve as additional training content for medical students for working with LBT patients within what has been traditionally termed “women’s cancers”. This project serves to evaluate the impact of the Cancer’s Margins videos on the self-reported confidence of Dalhousie University medical students in working with these populations. Medical students were invited to participate in a two-part online survey using a 29-item survey that explored self-rated confidence before and after watching the Cancer’s Margins videos. There were 4 open-ended questions for feedback on the videos to help assess overall self-rated impact. 23 surveys were either fully or partially completed. Overall attitudes towards LBT patients were positive, but overall confidence was variable. There was an average increase of 8% in overall group self-rated confidence after watching the Cancer’s Margins videos. Incorporating training for working with LBT patients into the medical school curriculum could increase quality of care and break down barriers in access to care for LBT populations. The Cancer’s Margins training videos can be an effective resource for medical students for increasing self-reported confidence.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".