The Ontario Pharmacy Evidence Network Atlas of Community Pharmacy Influenza Immunizations
Bibliographic record
Abstract
Influenza is an important public health concern, especially among vulnerable populations such as young children, seniors, individuals with chronic medical conditions and immunocompromised patients.<sup>1</sup> It is estimated that influenza infection leads to 12,200 hospitalizations and 3500 deaths annually in Canada.<sup>2</sup> Fortunately, vaccination can prevent influenza infection and the spread of the virus. A vaccine is developed annually for each influenza season based on the circulating viral strains expected to be dominant during the season.<sup>3</sup> It can take up to 2 weeks after administration before an influenza immunization is effective in providing protection against influenza,<sup>1</sup> and thus early immunization each influenza season is important. Publicly funded community pharmacy influenza programs exist in all Canadian provinces and 1 territory (Table 1). Alberta and British Columbia were the first to support publicly funded community pharmacy influenza immunization programs, offering the service since the 2009/2010 influenza season. At the other extreme, Quebec and the Yukon offered the service for the first time this 2020/2021 influenza season, and no service exists in the Northwest Territories or Nunavut. The purpose of this research brief is to introduce the Ontario Pharmacy Evidence Network (OPEN) Atlas of Community Pharmacy Influenza Immunizations tool and describe the uptake of influenza immunization services in Ontario over time. Our case example may help support pharmacy influenza immunization service planning and similar immunization planning for coronavirus disease 2019 (COVID-19).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 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 teacher head, 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".