Knowledge, Attitudes and Practices of Agricultural Workers towards Tetanus Vaccine: a Field Report.
Bibliographic record
Abstract
BACKGROUND: Agricultural Workers are both more exposed to tetanus and at higher risk to be inadequately immunized than other usual recipients of the same vaccine. STUDY DESIGN: Our cross-sectional questionnaire-based study aimed to evaluate tetanus vaccination status, knowledge, attitudes and practices in Agricultural Workers in North-Eastern Italy. METHODS: Bivariate and multivariate logistic regression analyses were used to identify, from individual and work-related characteristics, factors significantly associated with appropriate vaccination status. RESULTS: Among 707 participants, 58.4% had an up-to-date immunization status. In 33.1%, last booster was performed by an Emergency Department. The main reason for inadequate immunization was having forgotten the recommended periodic booster (146/707; 20.7%). Attitude towards tetanus vaccination was somehow favourable in 79.5% of participants, and 72.7% correctly identified tetanus vaccination as mandatory for Agricultural Workers. A lower degree of false beliefs and better knowledge of official recommendations were significant predictors of vaccine propensity. The main predictor for an appropriate vaccination status was interaction with a healthcare provider, in general (adjusted Odds Ratio, adjOR 2.516 95%CI 1.707-3.710), and specifically regarding vaccine counseling, (adjOR 6.275 3.184-12.367 and adjOR 9.739 95%CI 3.933-24.111 for general practitioners and occupational physicians, respectively). CONCLUSIONS: Our study enlightens the key role of healthcare providers in recalling and promoting vaccination policies, as well in increasing the general awareness of Agricultural Workers regarding vaccines and official recommendations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".