Attitudes towards homeless people among emergency department teachers and learners: a cross-sectional study of medical students and emergency physicians
Bibliographic record
Abstract
BACKGROUND: Medical students' attitudes and beliefs about homeless people may be shaped by the attitudes of their teachers and one of the most common sites for learning about homeless patients is the emergency department. The objective of this study was to determine if medical students in the preclinical and clinical years and emergency medicine faculty and residents have different attitudes and beliefs about homeless people. METHODS: The Health Professional Attitudes Toward the Homeless Inventory (HPATHI), was administered to all medical students, and emergency medicine physicians and residents at a large academic health sciences center in Canada. The HPATHI examines attitudes, interest and confidence on a 5-point Likert scale. Differences among groups were examined using the Kruskal Wallis test and Pearson's chi-square test. RESULTS: The HPATHI was completed by 371 individuals, for an overall response rate of 55%. Analysis of dichotomized median and percentage results revealed 5/18 statements were significant by both methods. On the attitudes subscales physicians and residents as a group were more negative for 2/9 statements and on the confidence subscale more positive for 1/4 statements. The interest subscale achieved overall statistical significance with decreased positive responses among physicians and residents compared to medical students in 2/5 statements. CONCLUSION: This study revealed divergences in attitudes, interests and beliefs among medical students and emergency medicine physicians and residents. We offer strategies for training interventions and systemic support of emergency faculty. Emergency medicine physicians can examine their role in the development of medical students through both formal and informal teaching in the emergency department.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".