Predicting Seasonal Influenza Vaccination Among Hospital-Based Nurses
Bibliographic record
Abstract
A descriptive cross-sectional online survey of a convenience sample of 202 hospital-based nurses was conducted to explore the factors associated with influenza vaccination. The findings suggest that the independent predictors of influenza vaccination were perception of job as a risk increasing factor (OR = 12.14; 95% CI [1.89, 78.08]), workplace vaccination clinics and campaigns (OR = 2.88; 95% CI [1.12, 7.38]), vaccination in the previous season (OR = 34.80; 95% CI [12.99, 93.28]), viewing vaccination as an inconvenience (OR = 0.22; 95% CI [0.07, 0.67]), and one's belief that the immune system provides better protection than the vaccine (OR = 0.29; 95% CI [0.11, 0.77]). In conclusion, the findings support the existing literature with regards to low vaccination rates among health care providers. Furthermore, the identification of the predictors of influenza vaccination among nurses may assist administrators and policy makers with the implementation of evidence-based vaccination strategies.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".