Influenza immunization among Canadian health care personnel: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Influenza immunization coverage among Canadian health care personnel remains below national targets. Targeting this group is of particular importance given their elevated risk of influenza infection, role in transmission and influence on patients' immunization status. We examined influenza immunization coverage in health care personnel in Canada, reasons for not being immunized and the impact of "vaccinate-or-mask" influenza prevention policies. METHODS: In this national cross-sectional study, we pooled data from the 2007 to 2014 cycles of the Canadian Community Health Survey and restricted it to respondents who reported a health care occupation. Using bootstrapped survey weights, we examined immunization coverage by occupation and by presence of vaccinate-or-mask policies, and reasons for not being immunized. We used modified Poisson regression to estimate the prevalence ratio (PR) of influenza immunization for health care occupations compared with the general working population. RESULTS: For all survey cycles combined, 50% of 18 446 health care personnel reported receiving seasonal influenza immunization during the previous 12 months, although this varied by occupation type (range 4%-72%). Compared with the general working population, family physicians and general practitioners were most likely to be immunized (PR 3.15, 95% confidence interval [CI] 2.76-3.59), whereas chiropractors, midwives and practitioners of natural healing were least likely (PR 0.17, 95% CI 0.10-0.30). Among those who were not immunized, the most frequently cited reason was the belief that influenza immunization is unnecessary. Introduction of vaccinate-or-mask policies was associated with increased influenza immunization among health care personnel. INTERPRETATION: Health care personnel are more likely to be immunized against influenza than the general working population, but coverage remains suboptimal overall, and we observed wide variation by occupation type. More efforts are needed to target specific health care occupations with low immunization coverage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".