Tailoring Interventions: Examining the Evidence and Identifying Gaps
Bibliographic record
Abstract
INTRODUCTION: Numerous population-based studies highlight the need to improve health care delivery and outcomes. Many single and combined interventions are available but their impact is limited and inconsistent. Tailoring may enhance their impact, but the best way to do so remains unclear. The purpose of this exploratory analysis was to identify potential ways to tailor these interventions that could enhance their effectiveness. METHODS: Interventions were chosen according to those included in a recent systematic review, which found that their impact was enhanced through tailoring. The most recent syntheses of research on the effectiveness of these interventions were identified in MEDLINE and examined for details of intervention design or delivery that influenced impact. RESULTS: Possible tailoring mechanisms were identified for 2 interventions. The impact of educational meetings could be enhanced by focusing on topics involving less complex behavior, offering a series of events, and including interactive components. The impact of audit and feedback could be enhanced by offering a series of events. Recent systematic reviews on the effectiveness of 3 interventions-self-assessment, public reporting of performance data, and opinion leaders-did not identify factors influencing their impact that could be used for tailoring. DISCUSSION: This exploratory review revealed few ways to potentially improve the effectiveness of interventions among the plethora of available trials. Nontraditional systematic reviews that consider research from different disciplines and featuring a variety of designs are recommended. More immediately, educators, professional associations, and health care managers could use this information to structure, implement, and support interventions that improve health care delivery and outcomes.
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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 | 0.236 | 0.460 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".