Pharmacy-based interventions to increase vaccine uptake: report of a multidisciplinary stakeholders meeting
Bibliographic record
Abstract
BACKGROUND: Despite the existence of efficacious vaccines, the burden of vaccine-preventable diseases remains high and the potential health benefits of paediatric, adolescent and adult vaccination are not being achieved due to suboptimal vaccine coverage rates. Based on emerging evidence that pharmacy-based vaccine interventions are feasible and effective, the European Interdisciplinary Council for Ageing (EICA) brought together stakeholders from the medical and pharmacy professions, the pharmaceutical industry, patient/ageing organisations and health authorities to consider the potential for pharmacy-based interventions to increase vaccine uptake. We report here the proceedings of this 3-day meeting held in March 2018 in San Servolo island, Venice, Italy, focussing firstly on examples from countries that have introduced pharmacy-based vaccination programmes, and secondly, listing the barriers and solutions proposed by the discussion groups. CONCLUSIONS: A range of barriers to vaccine uptake have been identified, affecting all target groups, and in various countries and healthcare settings. Ease of accessibility is a potentially modifiable determinant in vaccine uptake, and thus, improving the diversity of settings where vaccines can be provided to adults, for example by enabling community pharmacists to vaccinate, may increase the number of available opportunities for vaccination.
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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 | 0.049 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".