Overview of systematic reviews of the effectiveness of reminders in improving healthcare professional behavior
Bibliographic record
Abstract
OBJECTIVE: The purpose of this project was to conduct an overview of existing systematic reviews to evaluate the effectiveness of reminders in changing professional behavior in clinical settings. MATERIALS AND METHODS: Relevant systematic reviews of reminder interventions were identified through searches in MEDLINE, EMBASE, DARE and the Cochrane Library in conjunction with a larger project examining professional behavioral change interventions. Reviews were appraised using AMSTAR, a validated tool for assessing the quality of systematic reviews. As most reviews only reported vote counting, conclusions about effectiveness for each review were based on a count of positive studies. If available, we also report effect sizes. Conclusions were based on the findings from higher quality and current systematic reviews. RESULTS: Thirty-five reviews were eligible for inclusion in this overview. Ten reviews examined the effectiveness of reminders generally, 5 reviews focused on specific health care settings, 14 reviews concentrated on specific behaviors and 6 reviews addressed specific patient populations. The quality of the reviews was variable (median = 3, range = 1 to 8). Seven reviews had AMSTAR scores >5 and were considered in detail. Five of these seven reviews demonstrated positive effects of reminders in changing provider behavior. Few reviews used quantitative pooling methods; in one high quality and current review, the overall observed effects were moderate with an absolute median improvement in performance of 4.2% (IQR: 0.5% to 6.6%). DISCUSSION: The results support that modest improvements can occur with the use of reminders. The effect size is consistent with other interventions that have been used to improve professional behavior. CONCLUSION: Reminders appear effective in improving different clinical behaviors across a range of settings.
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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 | 0.042 | 0.120 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.026 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".