What are effective vaccine distribution approaches for equity-deserving and high-risk populations during COVID-19? Exploring best practices and recommendations in Canada: protocol for a mixed-methods multiple case codesign study
Bibliographic record
Abstract
INTRODUCTION: The WHO has stated that vaccine hesitancy is a serious threat to overcoming COVID-19. Vaccine hesitancy among underserved and at-risk communities is an ongoing challenge in Canada. Public confidence in vaccine safety and effectiveness and the principles of equity need to be considered in vaccine distribution. In Canada, governments of each province or territory manage their own healthcare system, providing an opportunity to compare and contrast distribution strategies. The overarching objective of this study is to identify effective vaccine distribution approaches and advance knowledge on how to design and implement various strategies to meet the different needs of underserved communities. METHODS AND ANALYSIS: Multiple case studies in seven Canadian provinces will be conducted using a mixed-methods design. The study will be informed by Experience-Based CoDesign techniques and theoretically guided by the Socio-Ecological Model and the Vaccine Hesitancy Matrix frameworks. Phase 1 will involve a policy document review to systematically explore the vaccine distribution strategy over time in each jurisdiction. This will inform the second phase, which will involve (2a) semistructured, in-depth interviews with policymakers, public health officials, researchers, providers, groups representing patients, researchers and stakeholders and (2b) an analysis of population-based administrative health data of vaccine administration. Integration of qualitative and quantitative data will inform the identification of effective vaccine distribution approaches for various populations. Informed by this evidence, phase 3 of the study will involve conducting focus groups with multiple stakeholders to codesign recommendations for the design and implementation of effective vaccine delivery strategies for equity-deserving and at-risk populations. ETHICS AND DISSEMINATION: This study is approved by the University of Toronto's Health Sciences Research Ethics Board (#42643), University of British Columbia Behavioural Research Ethics Board (#H22-01750-A002), Research Ethics Board of the Nova Scotia Health Authority (#48272), Newfoundland and Labrador Health Research Ethics Board (#2022.126), Conjoint Health Research Ethics Board, University of Calgary (REB22-0207), and University of Manitoba Health Research Board (H2022-239). The outcome of this study will be to produce a series of recommendations for implementing future vaccine distribution approaches from the perspective of various stakeholders, including equity-deserving and at-risk populations.
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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 | 0.083 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".