Bibliographic record
Abstract
Abstract Vaccination is one of the greatest public health successes. With sanitation and clean water, vaccines are estimated to have saved more lives over the past 100 years than any other health intervention. Vaccination not only protects the individual, but also, in many instances, provides community protection against vaccine-preventable diseases through herd immunity. To reduce the risk of vaccine-preventable diseases, vaccination programs rely upon reaching and sustaining high coverage rates, but paradoxically, because of the success of vaccination, new generations are often unaware of the risks of these serious diseases and their concerns now concentrate on the perceived risk of individual vaccines. Over the past decades, several vaccine controversies have occurred worldwide, generating concerns about vaccine adverse effects and eroding trust in health authorities, experts, and science. Gaps in vaccination coverage can, in part, be attributed to vaccine hesitancy and not just to “supply side issues” such as access to vaccination services and affordability. The concept of vaccine hesitancy is now commonly used in the discourse around vaccine acceptance. The World Health Organization defines vaccine hesitancy as “lack of acceptance of vaccines despite availability of vaccination services. Vaccine hesitancy is complex and context specific, varying across time, place and vaccines.” A vaccine-hesitant person can delay, be reluctant but still accept, or refuse one, some, or all vaccines. Technical, psychological, sociocultural, political, and economic factors can contribute to vaccine hesitancy. At the individual level, recent reviews have focused on factors associated with vaccination acceptance or refusal, identifying determinants such as fear of side effects, perceptions around health and prevention of disease and a preference for “natural” health, low perception of the efficacy and usefulness of vaccines, negative past experiences with vaccination services, and lack of awareness or knowledge about vaccination. Very few interventions have been shown to be effective in reducing vaccine hesitancy. Most of the studies have only focused on metrics of vaccine uptake and refusal to evaluate interventions aimed at enhancing vaccine acceptance, which makes it difficult to assess their potential effectiveness to address vaccine hesitancy. In addition, despite the complex nature of vaccination decision-making, the majority of public health interventions to promote vaccination are designed with the assumption that vaccine hesitancy is due to lack or inadequate knowledge about vaccines (the “knowledge-deficit” or “knowledge gap” approach). A key predictor of acceptance of a vaccine by a vaccine-hesitant person remains the recommendation for vaccination by a trusted healthcare provider. When providers communicate effectively about the value and need for vaccinations and vaccine safety, people are more confident in their decisions. However, to do this well, healthcare providers must be confident themselves about the safety, effectiveness, and importance of vaccination, and recent research has shown that a proportion of healthcare providers are vaccine-hesitant in their professional and personal lives. Effective strategies to address vaccine hesitancy among these hesitant providers have yet to be identified. A better understanding of the dynamics of the underlying determinants of vaccine hesitancy is critical for effective tailored interventions to be designed for both the public and healthcare providers.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".