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 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".