Feasibility and impact of providing feedback to vaccinating medical clinics: evaluating a public health intervention
Bibliographic record
Abstract
BACKGROUND: Vaccine coverage (VC) at a given age is a widely-used indicator for measuring the performance of vaccination programs. However, there is increasing data suggesting that measuring delays in administering vaccines complements the measure of VC. Providing feedback to vaccinators is recognized as an effective strategy for improving vaccine coverage, but its implementation has not been widely documented in Canada. The objective of this study was to evaluate the feasibility of providing personalized feedback to vaccinators and its impact on vaccination delays (VD). METHODS: In April and May 2008, a one-hour personalized feedback session was provided to health professionals in vaccinating medical clinics in the Quebec City region. VD for vaccines administered at two and twelve months of age were presented. Data from the regional vaccination registry were analysed for participating clinics. Two 12-month periods before and after the intervention were compared, namely from April 1st, 2007 to March 31st, 2008 and from June 1st, 2008 to May 31st, 2009. RESULTS: Ten medical clinics out of the twelve approached (83%), representing more than 2500 vaccinated children, participated in the project. Preparing and conducting the feedback involved 20 hours of work and expenses of $1000 per clinic. Based on a delay of one month, 94% of first doses of DTaP-Polio-Hib and 77% of meningococcal vaccine doses respected the vaccination schedule both before and after the intervention. Following the feedback, respect of the vaccination schedule increased for vaccines planned at 12 months for the four clinics that had modified their vaccination practices related to multiple injections (depending on the clinic, VD decreased by 24.4%, 32.0%, 40.2% and 44.6% respectively, p < 0.001 for all comparisons). CONCLUSIONS: The present study shows that it is feasible to provide personalized feedback to vaccinating clinics. While it may have encouraged positive changes in practice concerning multiple injections, this intervention on its own did not impact vaccination delays of the clinics visited. It is possible that feedback integrated into other types of effective interventions and sustained over time may have more impact on VD.
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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 | 0.026 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".