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Record W4404823144 · doi:10.1186/s12939-024-02326-w

Facilitators and challenges in collaboration between public health units and faith-based organizations to promote COVID-19 vaccine confidence in Ontario

2024· article· en· W4404823144 on OpenAlexafffundabout
Kadidiatou Kadio, Denessia Blake-Hepburn, Melodie Yunju Song, Anna Karbasi, Elizabeth Estey Noad, Samiya Abdi, Nazia Peer, Shaza A. Fadel, Sara Allin, Anushka Ataullahjan, Erica Di Ruggiero

Bibliographic record

VenueInternational Journal for Equity in Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsWestern UniversityToronto Public HealthCentre for Global Health ResearchPublic Health OntarioUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchFederation for the Humanities and Social SciencesCanadian Health Services Research Foundation
KeywordsPopulationPublic healthPublic relationsEthnic groupHealth equityCitizenshipDiversity (politics)SociologyPolitical scienceMedicineNursingEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Equitable access to vaccination remains a concern, particularly among population groups made structurally vulnerable. These population groups reflect the diversity of communities that are confronted with structural barriers caused by systemic racism and oppression and result in them experiencing suffer disadvantage and discrimination based on citizenship, race, ethnicity, ancestry, religion, spiritual beliefs, and/or gender identity. In Canada, Ontario public health units (PHUs) engage with faith-based organizations (FBOs) to improve vaccine confidence among populations made structurally vulnerable. This study explores the factors that facilitate and hinder engagement in the implementation of vaccine confidence promoting interventions, and challenges associated with working with FBOs. METHODS: In-depth interviews were conducted with 18 of the 34 Ontario PHUs who expressed an interest. Braun and Clarke's "experiential" approach was used to explore the realities of PHUs' contextual experiences and perspectives. RESULTS: The results showed that receptivity and openness of PHUs to learn from FBOs, previous experience working with religious communities and FBOs, ongoing relations based on respect of different beliefs and opinions on the vaccines, leveraging the support of trusted faith leaders among communities and communications strategy adapted and sensitive to the needs of the community was facilitators to community involvement in the prevention and control of COVID-19. On the other hand, factors both internal and external to the PHUs have often posed challenges to collaboration with the FBOs. Internal factors include low operational capacity of PHU like insufficient human and financial resources, weak analytical capacity, ambiguity in the roles and responsibilities of the different actors. Some external challenges issues were related to the provincial level and the Ministry of Health, while others were related to FBOs. For example, faith-based and collective beliefs promoting vaccine hesitancy have resulted in resistance from some religious communities when PHUs have reached out to collaborate. CONCLUSIONS: Engaging with faith-based communities is an ongoing process that requires time, flexibility, and patience, but it is necessary to improve vaccine confidence and equity access among population groups made structurally vulnerable. Lessons learned from this research can guide the implementation of future vaccination programs.

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)0.005
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.005
Scholarly communication0.0030.001
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.247
GPT teacher head0.472
Teacher spread0.225 · 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
Published2024
Admission routes3
Has abstractyes

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