Development and Implementation of Workshops to Optimize the Delivery of Vaccination Services in Community Pharmacies: Thinking beyond COVID-19
Bibliographic record
Abstract
Vaccines are widely recognized as the most economically efficient strategy to combat infectious diseases. Community pharmacists, being highly accessible healthcare professionals, have the potential to significantly contribute to the promotion and facilitation of vaccination uptake. In Canada, the jurisdiction of healthcare falls under provincial legislation, leading to variations in the extent of pharmacist practice throughout the country. While some pharmacists in Canada already functioned as immunizers, Québec pharmacists gained the authority to prescribe and administer vaccines in March 2020 amidst the COVID-19 pandemic. Our workshop aimed to equip pharmacists in Québec with the necessary guidance to optimize vaccinations, emphasizing the importance of maintaining and expanding immunization services beyond influenza and COVID-19 vaccines in the future. During the workshop, pharmacists had the opportunity to exchange valuable insights and best practices regarding workflow optimization, identifying areas for improvement in competency, effectively reaching vulnerable population groups, and integrating allied team members into their practice. Participants were also asked to develop a plan of action to help implement practice change beyond the workshop. Interactive workshops centered around discussions like these serve as catalysts for advancing the pharmacy profession, uniting professionals with a collective aim of enhancing patient care.
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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".