Learning to account for the social determinants of health affecting homeless persons
Bibliographic record
Abstract
CONTEXT: Intersecting social determinants of health constrain access to care and treatment adherence among homeless populations. Because clinicians seldom receive training in the social determinants of health, they may be unprepared to account for or address these factors when developing treatment strategies for homeless individuals. OBJECTIVES: This study explored: (i) clinicians' preparedness to provide care responsive to the social determinants of health in homeless populations, and (ii) the steps taken by clinicians to overcome shortcomings in their clinical training in regard to the social determinants of health. METHODS: Qualitative interviews were conducted with doctors (n = 6) and nurses (n = 18) in six Canadian cities. Participants had at least 2 years of experience in providing care to homeless populations. Interview transcripts were analysed using methods of constant comparison. RESULTS: Participants highlighted how, when first providing care to this population, they were unprepared to account for or address social determinants shaping the health of homeless persons. However, participants recognised the necessity of addressing these factors to situate care within the social and structural contexts of homelessness. Participants' accounts illustrated that experiential learning was critical to increasing capacity to provide care responsive to the social determinants of health. Experiential learning was a continuous process that involved: (i) engaging with homeless persons in multiple settings and contexts to inform treatment strategies; (ii) evaluating the efficacy of treatment strategies through continued observation and critical reflection, and (iii) adjusting clinical practice to reflect observations and new knowledge. CONCLUSIONS: This study underscores the need for greater emphasis on the social determinants of health in medical education in the context of homelessness. These insights may help to inform the development and design of service-learning initiatives that integrate understandings of the social determinants of health, and thus potentially improve the readiness of clinicians to address the complex factors that shape the health of homeless populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".