Distributing Publicly-Funded Influenza Vaccine—Community Pharmacies’ Perspectives on Acquiring Vaccines from Public Health and from Private Distributors in Ontario, Canada
Bibliographic record
Abstract
Objectives: To explore community pharmacies’ experience with two models of distribution for publicly-funded influenza vaccines in Ontario, Canada—one being publicly-managed (2015–2016 influenza season) and one involving private pharmaceutical distributors (2016–2017 season). Methods: Online surveys were distributed to community pharmacies across Ontario during the 2015–2016 and 2016–2017 influenza seasons with sampling proportional to Ontario Public Health Unit catchment populations. Quantitative data were analyzed descriptively and inferentially and qualitative data were summarized for additional context. Results: Order fulfillment appeared more responsive with the addition of private distributors in 2016–2017, as more pharmacies reported shorter order fulfillment times (p < 0.01); however, pharmacies reported significantly more days with zero on-hand inventory in 2016–2017 (p < 0.01), as well as more instances of patients being turned away due to vaccine unavailability (p < 0.05). In both seasons, a similar proportion of pharmacies reported slower order fulfillment and limited order quantities early in the season. Improved availability early in the season when patient demand is highest, more vaccines in a pre-filled syringe format, and better communication from distributors on product availability dates were recommended in qualitative responses. Conclusions: Introducing private distributors for the management and fulfillment of pharmacies’ orders for the publicly funded influenza vaccine appeared to have mixed results. While key concerns surrounding the frequency, responsiveness, and method of delivery were addressed by this change, challenges remain—in particular, acquiring sufficient vaccine early in the season to meet patient demand. As pharmacies become more prominent as vaccination sites, there are several opportunities to ensure that patient demand is met in this setting.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".