What is the role of primary care in the COVID-19 vaccine roll-out and the barriers and facilitators to an equitable vaccine roll-out? A rapid scoping review of nine jurisdictions
Bibliographic record
Abstract
OBJECTIVES: This study aimed to: (1) examine the experience of nine global jurisdictions that engaged primary care providers (PCPs) to administer COVID-19 vaccines during the pandemic; (2) describe how vaccine hesitancy and principles of equity were incorporated in the COVID-19 vaccine roll-out strategies and (3) identify the barriers and facilitators to the vaccine roll-out. DESIGN: Rapid scoping review. DATA SOURCES: Searches took place in MEDLINE, CINAHL, Embase, the Cochrane Library, SCOPUS and PsycINFO, Google, and the websites of national health departments. Searches and analyses took place from May 2021 to July 2021. RESULTS: Sixty-two documents met the inclusion criteria (35=grey literature; 56% and 27=peer reviewed; 44%). This review found that the vaccine distribution approach started at hospitals in almost all jurisdictions. In some jurisdictions, PCPs were engaged at the beginning, and the majority included PCPs over time. In many jurisdictions, equity was considered in the prioritisation policies for various marginalised communities. However, vaccine hesitancy was not explicitly considered in the design of vaccine distribution approaches. The barriers to the roll-out of vaccines included personal, organisational and contextual factors. The vaccine roll-out strategy was facilitated by establishing policies and processes for pandemic preparedness, well-established and coordinated information systems, primary care interventions, adequate supply of providers, education and training of providers, and effective communications strategy. CONCLUSIONS: Empirical evidence is lacking on the impact of a primary care-led vaccine distribution approach on vaccine hesitancy, adoption and equity. Future vaccine distribution approaches need to be informed by further research evaluating vaccine distribution approaches and their impact on patient and population outcomes.
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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 | 0.063 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".