A student-led curriculum framework for homeless and vulnerably housed populations
Bibliographic record
Abstract
BACKGROUND: Medical student demands for competency based homeless health education is increasing. Indeed, humans living homeless is a treatable health and social emergency. This innovation report outlines the initial development of an education framework for homeless health. METHODS: A medical student task force and educators conducted a mixed method study, including a scoping review of homeless health curriculum and competencies, a cross-country survey of medical students, and unique clinical guidelines. The task force collaborated with persons with lived experience and clinical guideline developers from the Homeless Health Research Network. The students presented at the Toronto Homeless Health Summit and refined the framework with feedback from homeless health experts. RESULTS: The main outcome was an evidence-based Homeless Health Curriculum Framework. It uses seven core competencies; with communication, advocacy, leadership, and upstream approaches playing the strongest roles. The framework integrated the new clinical guideline (housing, income assistance, case management and addiction). In addition, it identified approaches to support mental health care with trauma informed and patient centered care. It identified public health values, clinical objectives, and case studies. The framework aims to inform the design, delivery, service learning and evaluation for medical school curriculum. CONCLUSIONS: This student-led curriculum framework can support the design, implementation, delivery and evaluation of homeless health within the undergraduate medical curriculum. The framework can lay the foundation for new doctors, research and development; support consistency across programs; and support the creation of national learning and evaluation tools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".