An Inner City Emergency Medicine Rotation Does Not Improve Attitudes toward the Homeless among Junior Medical Learners
Bibliographic record
Abstract
Introduction Learners in the emergency department (ED) frequently encounter individuals who are homeless. We sought to evaluate the effect of an inner city emergency medicine rotation at the Royal Alexandra Hospital (RAH) on the attitudes of medical students and residents towards this population. Methods Data were collected both pre- and post-rotation using an electronic survey. Data collected included demographic information and as well as scores on the Health Professionals' Attitudes Towards the Homeless Inventory (HPATHI). Pre- and post-survey results were compared using Wilcoxon tests. Results Ninety-eight students completed the rotation. A total of 40 (41%) students completed both pre- and post-surveys. Demographic information was available for 66 respondents. Most participants were male (42 {64%}), single (47 {71%}), and 30 years of age or younger (59 {89%}). Most participants were of a Caucasian or East/South Asian background (61 {92%}) and grew up in an urban setting (51 {77%}). Overall, 43 (90%) participants saw at least one person who was homeless during their rotation. There was no significant difference between pre- and post-aggregate scores (z = -0.78, p = 0.44), or any of its three subscales (Personal Advocacy, Social Advocacy, and Cynicism). Conclusion First year residents and medical students are frequently exposed to patients who are homeless during an inner city ED rotation. Attitudes towards these patients did not significantly change following the rotation. Educational objectives should be set to improve attitudes of learners towards those with unstable housing throughout the ED rotation.
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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 | 0.001 | 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 | 0.000 | 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) | 0.005 | 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".