Homelessness and Vaccination Strategies: Problems and Potential Solutions to Vaccinate Vulnerable Populations
Bibliographic record
Abstract
Abstract People experiencing homelessness (PEH) are at a higher risk of vaccine-preventable illnesses. They have higher rates of chronic illnesses that predispose them to communicable diseases, and this is compounded by poor access to sanitation. While vaccination is especially important in PEH, they tend to have lower rates of vaccine uptake compared to the general population. Factors impacting this discrepancy include difficulty accessing vaccines and public health programs, lack of access to primary care services, and distrust of the health care system. Despite this, there is evidence to suggest that many PEH are accepting of vaccinations and are willing to get vaccinated provided the right approach and interactions. Understanding client-specific barriers along with education and counseling are key to improving vaccine uptake in PEH, and programs targeted specifically at PEH can improve vaccine uptake and ultimately the health of PEH.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".