Factors associated with influenza vaccination among healthcare workers in acute care hospitals in Canada
Bibliographic record
Abstract
BACKGROUND: Influenza vaccine coverage rates among healthcare workers (HCWs) in acute care facilities in Canada remain below national targets. OBJECTIVE: To determine factors associated with influenza vaccine uptake among HCWs. METHODS: This secondary analysis of a prospective cohort study included HCWs aged 18-69 years, working ≥20 h/wk in a Canadian acute care hospital. Questionnaires were administered to participants in the fall of the season of participation (2011/12-2013/14) which captured demographic/household characteristics, medical histories, occupational, behavioural and risk factors for influenza. Generalized estimating equation logistic regression was used to determine factors associated with vaccine uptake in the season of participation. RESULTS: The adjusted odds ratio for influenza vaccination in the current season was highest for those vaccinated in 3 of 3 previous seasons (OR 156; 95% CI 98, 248) followed by those vaccinated in 2 of 3 and 1 of 3 previous seasons when compared with those not vaccinated. Compared with nurses, physicians (OR 4.2; 95% CI 1.4, 13.2) and support services staff (OR 1.8; 95% CI 1.3, 2.4) had higher odds ratios for vaccine uptake. Conversely, HCWs identifying as Black had lower odds of uptake compared with those with European ancestry (OR 0.44, 95% CI 0.26-0.75) when adjusted for other factors in the model. CONCLUSION: Healthcare workers differ in their annual uptake of influenza vaccine based on their past vaccination history, occupation and ethnicity. These findings indicate a need to determine whether there are other vaccine-hesitant groups within healthcare settings and learn which approaches are successful in increasing their uptake of influenza vaccines.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".