In this digital age, how easily accessible is pharmacist vaccination information? The case of New Zealand
Bibliographic record
Abstract
Background: Pharmacist-led vaccination that has the potential to ease the vaccination burden from general practitioners, is comparatively a newer service in New Zealand. However, to reap the maximum benefits out of this service, a consistent and effective promotion approach using various online platforms is indispensable. Objective: To identify what online information the general public can find about which pharmacies across NZ provide vaccination services. Methods: Every pharmacy in NZ was reviewed online to determine what vaccination information they advertised, then a sample of pharmacies were randomly selected from six District Health Boards (DHBs) to be called and confirm if the information they stated online was accurate. Results: Whilst the majority (more than 70%) of pharmacies did provide information about their services online, only 31% of the pharmacies had vaccination information on their websites, 20% on Healthpoint, and 13% had the information on social media. The telephonic survey revealed various information discrepancies in more than a quarter of the sample. Conclusions: A lack of online presence across multiple pharmacies is a pressing issue. Also, currently, NZ pharmacies do not have a very high online presence advertising vaccination services. Improving the amount and quality of this information is pertinent at this time as when COVID-19 vaccination drive may commence anytime, and the pharmacy sector will be well placed to conduct vaccinations on a large scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".