Vaccine hesitancy: Moving practice beyond binary vaccination outcomes in community pharmacy
Bibliographic record
Abstract
Unequivocally, vaccinations can be considered one of the greatest global achievements for public health. Since the introduction of the first vaccines, vaccination programs have contributed to a substantial decline in both mortality and morbidity of many previously lethal infectious diseases around the globe.1 However, high and sustained vaccine uptake is necessary for these efforts to remain successful. Beyond the direct protection provided for vaccinated individuals, high vaccine coverage also induces indirect protection against vaccine-preventable diseases (VPD) at a population level through herd immunity.2 Yet despite consensus on the public health benefits of vaccination, recent reports of clustered outbreaks and the resurgence of VPDs in under- or nonimmunized groups3 highlight the ongoing challenges. The concept of vaccine hesitancy represents a significant conceptual shift away from the traditional dichotomy of accept or reject, of pro- or antivaccine. Defined by the World Health Organization’s (WHO) SAGE Working Group as “the delay in acceptance or refusal of vaccination despite availability of vaccination services,” vaccine hesitancy is “complex and context specific, varying across time, place and vaccines. It is influenced by factors such as complacency, convenience and confidence.”4 Vaccine hesitancy highlights the wide spectrum of vaccination beliefs, attitudes and behaviours of a large heterogeneous group of individuals situated between the 2 end points of the continuum (see Figure 1). For example, vaccine-hesitant individuals may refuse some vaccines but accept or delay others; some may accept a vaccine but remain concerned with their decision; and others may even personally refuse all vaccines but remain supportive of vaccinations more broadly. While vaccine hesitancy is not always the root cause of under- or nonimmunization, it is listed in the top 10 threats to global health for 2019 by the WHO5 and remains an important contributor to suboptimal vaccine coverage across many jurisdictions.
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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 | 0.026 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".