Pharmacists as Immunizers: The Role of Pharmacies in Promoting Immunization Campaigns and Counteracting Vaccine Hesitancy
Bibliographic record
Abstract
Vaccines represent fundamental public health interventions aimed to counteract or, at least, partially mitigate the severe epidemiological and economic burden generated by communicable disorders, in terms of (i) outcome-related, (ii) behavior-related productivity gains, and (iii) community externalities in developed settings as well as in developing countries. Despite their importance, several parents choose not to immunize their children due to the rising phenomenon of anti-vaccination movements that divulge vaccine-related "fake news" and "post-modern, post-factual truths". Vaccine hesitancy represents a threat that can seriously jeopardize the implementation and success of vaccination campaigns. Within this framework, from a public health perspective, community pharmacies can play a vital role in that pharmacists can: (i) act as immunizers (vaccine distributors, educators, facilitators and administrators), (ii) improve vaccine-related health literacy and vaccination coverage rates as well as (iii) remove barriers and obstacles to the access to healthcare settings offering immunization services and (iv) counteract vaccine hesitancy.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".