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Record W4307340617 · doi:10.9778/cmajo.20210248

Determinants of SARS-CoV-2 vaccine willingness among people incarcerated in 3 Canadian federal prisons: a cross-sectional study

2022· article· en· W4307340617 on OpenAlexafffundvenueabout
Kathryn Romanchuk, Blake Linthwaite, Joseph Cox, Hyejin Park, Camille Dussault, Nicole E. Basta, Olivia Varsaneux, James Worthington, Bertrand Lebouché, Shannon E. MacDonald, Shainoor J. Ismail, Nadine Kronfli

Bibliographic record

VenueCMAJ Open · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsMcGill UniversityCentre For Cold Ocean Resources EngineeringPublic Health Agency of CanadaMcGill-Queen's University PressMcGill University Health CentreUniversity of Alberta
FundersNational Institutes of HealthCanadian Institutes of Health ResearchMerck CanadaViiV HealthcareMcGill UniversityGilead Sciences
KeywordsMedicineOdds ratioVaccinationInfluenza vaccineLogistic regressionConfidence intervalOddsPrisonDemographyImmunologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Maximizing uptake of SARS-CoV-2 vaccines among people in prison is essential in mitigating future outbreaks. We aimed to determine factors associated with willingness to receive SARS-CoV-2 vaccination before vaccine availability. METHODS: We chose 3 Canadian federal prisons based on their low uptake of influenza vaccines in 2019-2020. Participants completed a self-administered questionnaire on knowledge, attitude and beliefs toward vaccines. The primary outcome was participant willingness to receive a SARS-CoV-2 vaccine, measured using a 5-point Likert scale to the question, "If a safe and effective COVID-19 vaccine becomes available in prison, how likely are you to get vaccinated?" We calculated the association of independent variables (age, ethnicity, chronic health conditions, 2019-2020 influenza vaccine uptake and prison security level), identified a priori, with vaccine willingness using logistic regression and crude and adjusted odds ratios (ORs) with 95% confidence intervals (CIs). RESULTS: We recruited 240 participants from Mar. 31 to Apr. 19, 2021 (median age 46 years; 19.2% female, 25.8% Indigenous). Of these, 178 (74.2%) were very willing to receive a SARS-CoV-2 vaccine. Participants who received the 2019-2020 influenza vaccine (adjusted OR 5.20, 95% CI 2.43-12.00) had higher odds of vaccine willingness than those who did not; those who self-identified as Indigenous (adjusted OR 0.27, 95% CI 0.11-0.60) and in medium- or maximum-security prisons (adjusted OR 0.36, 95% CI 0.12-0.92) had lower odds of vaccine willingness than those who identified as white or those in minimum-security prisons, respectively. INTERPRETATION: Most participants were very willing to receive vaccination against SARS-CoV-2 before vaccine roll-out. Vaccine promotion campaigns should target groups with low vaccine willingness (i.e., those who have declined influenza vaccine, identify as Indigenous or reside in high-security prisons).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.032
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.386
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2022
Admission routes4
Has abstractyes

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