Interventions on health care providers to improve seasonal influenza vaccination rates among patients: a systematic review and meta-analysis of the evidence since 2000
Bibliographic record
Abstract
BACKGROUND: Seasonal influenza vaccination (SIV) rates remain suboptimal in many populations, even in those with universal SIV. OBJECTIVE: To summarize the evidence on interventions on health care providers (physicians/nurses/pharmacists) to increase SIV rates. METHODS: We systematically searched/selected full-text English publications from January 2000 to July 2019 (PROSPERO-CRD42019147199). Our outcome was the difference in SIV rates between patients in intervention and non-intervention groups. We calculated pooled difference using an inverse variance, random-effects model. RESULTS: We included 39 studies from 8370 retrieved citations. Compared with no intervention, team-based training/education of physicians significantly increased SIV rates in adult patients: 20.1% [7.5-32.7%; I2 = 0%; two randomized controlled trials (RCTs)] and 13.4% [8.6-18.1%; I2 = 0%; two non-randomized intervention studies (NRS)]. A smaller increase was observed in paediatric patients: 7% (0.1-14%; I2 = 0%; two NRS), and in adult patients with team-based training/education of physicians and nurses together: 0.9% (0.2-1.5%; I2 = 30.6%; four NRS). One-off provision of guidelines/information to physicians, and to both physicians and nurses, increased SIV rates in adult patients: 23.8% (15.7-31.8%; I2 = 45.8%; three NRS) and paediatric patients: 24% (8.1-39.9%; I2 = 0%; two NRS), respectively. Use of reminders (prompts) by physicians and nurses slightly increased SIV rates in paediatric patients: 2.3% (0.5-4.2%; I2 = 0%; two RCTs). A larger increase was observed in adult patients: 18.5% (14.8-22.1%; I2 = 0%; two NRS). Evidence from both RCTs and NRS showed significant increases in SIV rates with varied combinations of interventions. CONCLUSIONS: Limited evidence suggests various forms of physicians' and nurses' education and use of reminders may be effective for increasing SIV rates among patients.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".