Improving Influenza Vaccination Coverage: A Quality Improvement Project in Internal Medicine Clinic
Bibliographic record
Abstract
Background: The influenza vaccine coverage is low despite its proven benefit in improving morbidity and mortality, and costs associated with influenza infection. Primary care clinics provide a convenient and effective space to conduct a discussion related to influenza vaccine. A decrease in the influenza vaccine coverage was noticed during the coronavirus disease 2019 (COVID-19) pandemic along with a decrease in patient visits. Methods: The quality improvement study was done at a primary care clinic in an underserved area over a 12-week period. Providers were given education in a group setting at the beginning of every alternate week, i.e., 6 weeks duration, and the remaining 6 weeks served as a comparison group. Further, a brief education was given at the beginning of the week, and a simple questionnaire was given before each patient visit. The providers were advised to record their discussion in the electronic medical records. Results: The intervention led to increased discussion regarding the influenza vaccine between the providers and the patients (χ 2 (1, N = 726) = 25.76, P < 0.00001 without Yates correction), but no statistically significant difference was noticed in the proportion of patients accepting the vaccine (χ 2 (1, N = 187) = 1.714, P = 0.1905 without Yates correction). Conclusion: Providers were least likely to offer the vaccine on visits scheduled for a pre-operative evaluation or an acute complain. A simple intervention like providing education to healthcare staff can significantly improve discussions regarding vaccines, and has the potential to improve coverage. Clin Infect Immun. 2024;9(1):11-15 doi: https://doi.org/10.14740/cii176
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".