Reducing physician voiding cystourethrogram ordering in children with first febrile urinary tract infection: evaluation of a purposefully sequenced educational intervention
Bibliographic record
Abstract
BACKGROUND: Physicians often fail to implement clinical practice guidelines. Our aim was to evaluate whether a purposefully sequenced, multifaceted educational intervention would increase physician adherence to a guideline for voiding cystourethrogram (VCUG) use following first urinary tract infection (UTI) in young children. METHODS: Using a single centre, pretest-posttest design, we compared the proportion of guideline adherent VCUG orders and the VCUG ordering rate before and after three educational interventions (interactive lecture, clinical pathway, faxed reminder) selected and sequenced according to the PRECEDE (Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation) health promotion model. RESULTS: One hundred and nine physicians ordered 219 VCUGs for 219 children. Following the interventions, there was an increase in the monthly proportion of adherent VCUGs ordered by pediatricians (analysis of variance (ANOVA) F(2,29) = 3.38, p = .048) and non-pediatricians (ANOVA F(2,28) = 14.71, p < .001). Also, pediatricians decreased their monthly VCUG ordering rate (linear trend incidence rate ratio 0.74, 95% confidence interval (CI) [0.54, 0.99]). Pediatricians were more likely to adhere with the guideline than were non-pediatricians (odds ratio 2.91, 95% CI [1.5, 5.5]). CONCLUSION: Exposure to purposefully sequenced educational interventions based on the PRECEDE model was associated with increased adherence to guideline recommendations.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".