Harnessing the power of the community to increase vaccination among homeless populations in inner-city Vancouver
Bibliographic record
Abstract
Aim: Homeless populations have lower vaccination uptake than the general population. Low-threshold clinics play a crucial role in providing preventative interventions to these individuals. The objective of this study was to determine the effectiveness of using a community pop-up clinic to achieve high levels of vaccination among residents of Vancouver's inner-city.Methods: In this study, we implemented mobile community pop-up vaccine clinics to provide vaccinations to individuals residing at single-room occupancy dwellings in inner-city Vancouver, Canada. We provided education about respiratory infection vaccines, then offered immunization for COVID-19, influenza and pneumococcus to all who were eligible.Results: From October 2023 to April 2024, we held 34 events, and engaged 335 under-housed residents of Vancouver's inner-city. We administered 527 vaccines: 237, 221 and 69 influenza, COVID-19 and pneumococcal, respectively. For COVID-19, and a total of 221 doses were given to 210 individuals; and this represented the first immunization for 15 individuals. The smaller number of pneumococcal vaccines given was related to the later implementation of this aspect of our program.Conclusion: Achieving higher vaccination rates among Vancouver's inner-city residents requires targeted initiatives and low-barrier access. Our mobile pop-up clinic program has led to high vaccination rates among under-housed inner-city residents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".