Impact of educational interventions on the prevention of influenza: A systematic review
Bibliographic record
Abstract
Introduction: Seasonal influenza, a contagious viral disease affecting the upper respiratory tract, circulates annually, causing considerable morbidity and mortality. The present study investigates the effectiveness of educational interventions to prevent influenza. Methods: We searched PubMed/Medline, Embase, and Cochrane Controlled Register of Trials (CENTRAL) for relevant clinical studies up to March 1 2022. The following terms were used: "influenza," "flu," "respiratory infection," "prevent," "intervention," and "education." Results: Out of 255 studies, 21 articles satisfied the inclusion criteria and were included in our study: 13 parallel randomized controlled trials (RCT) studies, two cross-over RCT studies, two cohort studies, and four quasi-experimental studies. A total of approximately 12,500 adults (18 years old or above) and 11,000 children were evaluated. Educational sessions and reminders were the most common interventions. The measured outcomes were vaccination rates, the incidence of respiratory tract infection (RTI), and preventive behaviors among participants. Eighteen out of 21 articles showed a significant association between educational interventions and the outcomes. Conclusions: The included studies in the current systematic review reported the efficacy of health promotion educational interventions in improving knowledge about influenza, influenza prevention behaviors, vaccination rates, and decreased RTI incidence regardless of the type of intervention and the age of cases.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".