Protecting our patients by protecting ourselves
Bibliographic record
Abstract
BACKGROUND: With recent expansions to scope of practice that have allowed Canadian pharmacists to play a larger role in administering influenza vaccinations to the public, it is important that pharmacists themselves meet Canadian guidelines recommending that 80% of health care professionals and 100% of vaccinators receive an annual influenza vaccination. Unvaccinated health care professionals pose an infection risk to patients they serve and are at an increased risk of infection themselves. METHODS: An online, anonymous survey was sent to Ontario community pharmacists to determine whether they had received the influenza vaccination during the 2013-2014 influenza season. All significant univariate chi-square analysis respondent characteristics were included in a multivariate regression analysis model to determine predictors of vaccination status. RESULTS: A total of 780 pharmacists completed the survey (18.1% response rate), which showed that 7 in 10 Ontario community pharmacists received the influenza vaccine. Those certified to immunize were nearly 3 times more likely to have received the influenza vaccine than those not certified (81.6% versus 61.2%, respectively). DISCUSSION: Having 70% of Ontario community pharmacists vaccinated against influenza is both an accomplishment and an opportunity to improve vaccination rates. While similar to the influenza immunization rates of other health care professions, Ontario community pharmacists did not meet Public Health Canada's recommendations. Comprehensive worksite programs, including promotion, education and convenient access to influenza vaccination at no cost, could increase community pharmacist influenza vaccination rates. CONCLUSION: The authors issue a call to arms to encourage all pharmacists to receive the influenza vaccine to protect the public and themselves.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.034 | 0.020 |
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".