Attitude change of medical students towards people facing homelessness through mandatory rotation
Bibliographic record
Abstract
Background Addressing disparities of care and understanding the reality of patients in vulnerable situations are increasingly recognized as fundamental objectives for Canadian physicians. However the hidden curricula in medical schools can contribute to a culture of discrimination for the patients in the most vulnerable situations, as future doctors express more negative attitudes towards these patients at the end of medical school than at the beginning. Objectives and Methods It is the first impact assessment of a mandatory rotation on medical students' attitude towards homelessness. A group of students at Université de Montréal have built in 2013 a one-week mandatory rotation for medical students consisting of courses, discussions and experiential learning in the community with patients in vulnerable situations. Our research evaluated the impact of this rotation on medical student’s attitude towards people facing homelessness. We used a translated version of the Health Professionals’ Attitudes Towards the Homeless Inventory (HPATHI) questionnaire to assess the attitude of medical students regarding people facing homelessness before their rotation, after it and before starting residency. Basic demographic data were also collected to control for any confounding factors that could affect changes in attitude. Results The rotation was given to 271 students. 139 students handed in both pre and post questionnaires and were included in the study. The average post-rotation HPATHI score was significantly higher than the average pre-rotation score with an average improvement of 0.15 on a Likert scale of 5 points (CI 95% [0,11 - 0,20], p < 0.0001). Conclusions While the medical curriculum is increasingly oriented towards future physicians’ attitudes, coming up with concrete means to improve these attitudes and to measure this improvement can prove cumbersome. We discuss the way this course was built and how we can better measure the impact of similar projects. Key messages: Experiential learning combined with courses and oriented discussions can bring long term attitude improvement. Impact of projects designed to bring attitude improvement can be measured.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".