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Record W4404542950 · doi:10.1080/17460794.2024.2421666

Harnessing the power of the community to increase vaccination among homeless populations in inner-city Vancouver

2024· article· en· W4404542950 on OpenAlexaffabout
Shana Yi, Christina Wiesmann, David Truong, Shawn Sharma, Brian E. Conway

Bibliographic record

VenueFuture Virology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSimon Fraser UniversityVancouver Infectious Diseases Centre
Fundersnot available
KeywordsVaccinationGeographyInner cityPower (physics)DemographySocioeconomicsEnvironmental healthGerontologyMedicineVirologyEconomic geographySociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.389
Teacher spread0.346 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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