Seasonal influenza self-vaccination behaviours and attitudes among nurses in Southeastern France
Bibliographic record
Abstract
BACKGROUND: Despite seasonal influenza vaccination (SIV) being recommended to healthcare professionals to protect themselves and their patients, uptake is low, especially among nurses. We sought to study self-vaccination behaviours, attitudes and knowledge about SIV among nurses in Southeastern France. METHODS: A cross-sectional survey with community and hospital-based hospital nurses was conducted with the same standardised questionnaire. Multi-model averaging approaches studied factors associated with the following dependent variables: self-reported SIV uptake; and considering SIV a professional responsibility. RESULTS: 1539 nurses completed the questionnaire (response rate: 85%). SIV was the most frequently cited vaccine (49%) regarding nurses' unfavourable opinions towards specific vaccines. Thirty-four percent of nurses reported being vaccinated at least once during the 2015-2016 or 2016-2017 seasons. A lack of perceived personal vulnerability to influenza, a fear of adverse effects, and a preference for homeopathy constituted the main deterrents of SIV. Nurses held various misconceptions about the SIV, but 69% considered its benefits to be greater than its risks. The multi-model averaging approach showed that considering SIV as a professional responsibility was the main factor associated with SIV uptake among nurses (Nagelkerke's partial R-squared: 15%). This sense of responsibility was strongly associated with trust in various vaccine information sources. CONCLUSION: Nurses had low SIV uptake rates and held various concerns and a lack of knowledge surrounding the vaccine. This is concerning considering the impact that these factors can have on nurses and patients' health, especially considering the increased role that nurses could have surrounding SIV in the near future.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".