Homelessness in the Medical Curriculum: An Analysis of Case-Based Learning Content From One Canadian Medical School
Bibliographic record
Abstract
UNLABELLED: PHENOMENON: Homelessness is a major public health concern. Given that homeless individuals have high rates of mortality and morbidity, are more likely to be users of the healthcare system, and often report unmet health needs, it is important to examine how homelessness is addressed in medical education. We wanted to examine content and framing of issues related to homelessness in the case-based learning (CBL) curriculum and provide insights about whether medical students are being adequately trained to meet the health needs of homeless individuals through CBL. APPROACH: CBL content at a Canadian medical school that featured content related to homelessness was analyzed. Data were extracted from cases for the following variables: curriculum unit (e.g., professionalism/ethics curriculum or biomedical/clinical curriculum), patient characteristics (e.g., age, sex), and medical and social conditions. A thematic analysis was performed on cases related to homelessness. Discrepancies in analysis were resolved by consensus. FINDINGS: Homelessness was mentioned in five (2.6%) of 191 CBL cases in the medical curriculum. Homelessness was significantly more likely to be featured in professionalism/ethics cases than in biomedical/clinical cases (p = .03). Homeless patients were portrayed as socially disadvantaged individuals, and medical learners were prompted to discuss ethical issues related to homeless patients in cases. However, homeless individuals were largely voiceless in cases. Homelessness was associated with serious physical and mental health concerns, but students were rarely prompted to address these concerns. Insights: The health and social needs of homeless individuals are often overlooked in CBL cases in the medical curriculum. Moreover, stereotypes of homelessness may be reinforced through medical training. There are opportunities for growth in addressing the needs of homeless individuals through medical education.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".