Identifying vaccination deserts: The availability and distribution of pharmacists with authorization to administer injections in Ontario
Bibliographic record
Abstract
Introduction:Pharmacist-administered immunizations have been associated with improved vaccination rates; however, little is known about whether areas with little to no access to this service (“vaccination deserts”) exist. The objective of this work is to determine the geographic availability of pharmacists with authorization to administer injections in the province of Ontario.Methods:Ontario College of Pharmacists registry data were used to identify patient care–providing pharmacists in community pharmacies and their ability to administer injections. Their number of hours worked was converted into full-time equivalents (FTEs), assuming 40 hours per week represents 1 FTE. Practice site(s) were mapped by postal code and presented by Public Health Unit (PHU) area. Communities within PHUs were further categorized as urban or rural and northern or southern, with ratios of FTEs per 1000 population calculated for both injection-trained and non-injection-trained pharmacists.Results:In total, 74.6% of Ontario’s practising community pharmacists are authorized to provide injections. Northern PHUs had slightly better access to pharmacist injectors (0.61 FTEs/1000 overall vs 0.56/1000 in the south), while rural communities had lower availability (0.41 FTEs/1000) than urban communities (0.58 FTEs/1000). PHUs with greater population size and density had greater availability of pharmacist immunizers, while PHUs with greater land area were more likely to not have any immunizing pharmacists present (p < 0.001 for all).Discussion:As pharmacists increasingly become preferred vaccination providers, awareness of disparities related to access to pharmacy-based immunizations and collaboration with public health and primary care providers to address them (e.g., through mobile vaccination clinics) will be required to ensure equitable access. Can Pharm J (Ott) 2022;155:xx-xx.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".