Faith-based organisations and their role in supporting vaccine confidence and uptake: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Faith-based organisations (FBOs) and religious actors increase vaccine confidence and uptake among ethnoracially minoritised communities in low-income and middle-income countries. During the COVID-19 pandemic and the subsequent vaccine rollout, global organisations such as the WHO and UNICEF called for faith-based collaborations with public health agencies (PHAs). As PHA-FBO partnerships emerge to support vaccine uptake, the scoping review aims to: (1) outline intervention typologies and implementation frameworks guiding interventions; (2) describe the roles of PHAs and FBOs in the design, implementation and evaluation of strategies and (3) synthesise outcomes and evaluations of PHA-FBO vaccine uptake initiatives for ethnoracially minoritised communities. METHODS AND ANALYSIS: We will perform six library database searches in PROQUEST-Public Health, OVID MEDLINE, Cochrane Library, CINAHL, SCOPUS- all, PROQUEST - Policy File index; three theses repositories, four website searches, five niche journals and 11 document repositories for public health. These databases will be searched for literature that describe partnerships for vaccine confidence and uptake for ethnoracially minoritised populations, involving at least one PHA and one FBO, published in English from January 2011 to October 2023. Two reviewers will pilot-test 20 articles to refine and finalise the inclusion/exclusion criteria and data extraction template. Four reviewers will independently screen and extract the included full-text articles. An implementation science process framework outlining the design, implementation and evaluation of the interventions will be used to capture the array of partnerships and effectiveness of PHA-FBO vaccine uptake initiatives. ETHICS AND DISSEMINATION: This multiphase Canadian Institutes of Health Research (CIHR) project received ethics approval from the University of Toronto. Findings will be translated into a series of written materials for dissemination to CIHR, and collaborating knowledge users (ie, regional and provincial PHAs), and panel presentations at conferences to inform the development of a best-practices framework for increasing vaccine confidence and uptake.
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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 | 0.137 | 0.112 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.016 |
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".