Interventions to increase influenza vaccination rates of those 60 years and older in the community
Bibliographic record
Abstract
BACKGROUND: Although the evidence to support influenza vaccination is poor, it is promoted by many health authorities. There is uncertainty about the effectiveness of interventions to increase influenza vaccination rates in those 60 years or older. OBJECTIVES: To assess effects of interventions to increase influenza vaccination rates in those 60 or older. SEARCH STRATEGY: We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library, 2010, issue 3), containing the Cochrane Acute Respiratory Infections Group's Specialized Register, MEDLINE (January 1950 to July 2010), PubMed (January 1950 to July 2010), EMBASE (1980 to 2010 Week 28), AgeLine (1978 to July 2010), ERIC (1965 to July 2010) and CINAHL (1982 to July 2010). SELECTION CRITERIA: Randomized controlled trials (RCTs) to increase influenza vaccination rates in those aged 60 years and older, recording influenza vaccination status either through clinic records, billing data or local/national vaccination registers. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed study quality and extracted data. MAIN RESULTS: Forty-four RCTs were included. All included RCTs studied seniors in the community and in high-income countries. No RCTs of society-level interventions were included. Heterogeneity was marked and meta-analysis was limited. Only five RCTs were graded at low and six at moderate risk of bias. They included three of 13 personalized postcard interventions (all three with the 95% confidence interval (CI) above unity), two of the four home visit interventions (both with 95% CI above unity, but one a small study), three of the four reminder to physicians interventions (none with 95% CI above unity) and three of the four facilitator interventions (one with 95% CI above unity, and one P < 0.01). The other 33 RCTs were at high risk of bias and no recommendations for practice can be drawn. AUTHORS' CONCLUSIONS: Personalized postcards or phone calls are effective, and home visits, and facilitators, may be effective. Reminders to physicians are not. There is insufficient good evidence for other interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".