Herpes zoster and human papillomavirus vaccination opportunities identified using electronic prompts at the time of scheduling influenza or COVID-19 vaccines
Bibliographic record
Abstract
Background: Due to workload and competing priorities, vaccination-related interactions in community pharmacies tend to be more reactive than proactive. The aim of this study is to determine the proportion of users of a web-based scheduling system for influenza and COVID-19 vaccines who may be eligible for herpes zoster or human papillomavirus (HPV) vaccination and interested in discussing these vaccines with a pharmacist. Methods: Individuals scheduling an influenza or COVID-19 vaccine at a pharmacy using the MedEssist platform between October 2021 and March 2022 were asked about their vaccination status against HPV (if aged 9-45) or herpes zoster (if aged ≥50). Those who were unvaccinated or unsure were asked to indicate their willingness to discuss this with a pharmacist. Logistic regression was performed to identify patient characteristics associated with responses to these screening questions. Results: Among 36,659 bookings by those aged 9 to 45 and 55,728 by those aged ≥50 that included responses to screening questions, 70.1% and 55.5% were potentially unvaccinated against HPV and herpes zoster, respectively, with approximately 1 in 5 also indicating willingness to have a discussion with the pharmacist. Those scheduling appointments for COVID-19 vaccines were significantly less likely to be vaccinated against HPV or herpes zoster and less willing to discuss this with a pharmacist than those seeking influenza vaccination. Discussion: Automated prompts while booking influenza or COVID-19 vaccinations have the potential to identify vaccine-willing individuals who may benefit from further discussion on their vaccination needs. Conclusion: Community pharmacies can leverage available technology to support the efficient and effective identification of individuals eligible 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".