A Scoping Review of Medical Students’ Perception of People Experiencing Homelessness
Bibliographic record
Abstract
Medical education plays a crucial role in shaping future healthcare providers' attitudes, skills, and understanding of underserved populations, including those experiencing homelessness. Summarizing the existing literature on medical students’ perception of People Experiencing Homelessness (PEH) can aid medical educators in developing a curriculum to allow medical students to comprehensively care for this population. The databases Web of Science, ERIC, Psychinfo, Embase and Medline were searched from 1946 to June 2023. Published quantitative and qualitative studies reporting on the perceptions or attitudes of medical students towards PEH were included. The studies were analyzed with descriptive statistics and described narratively. The search identified 1667 studies, with 15 ultimately included. Of the included studies, 66.7% were conducted in the United States of America, 26.7% were conducted in Canada, and 6.7% were completed in the United Kingdom. These studies revealed that medical students' perceptions vary, although most studies found medical students possess positive attitudes towards PEH, albeit feeling uncomfortable in their ability to provide care to this population. Additionally, most studies found when medical students were given educational exposures to PEH, they reported improved attitudes, more positive perceptions, and increased confidence in providing care. These results emphasize the benefits of providing structured educational experiences with PEH to medical students for improving patient care. More regionally diverse research in addition to studies examining the long-term effects of exposure to PEH during medical education on subsequent professional attitudes and behaviours is necessary.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".