What Interventions Should Pharmacists Employ to Impact Health Practitioners’ Prescribing Practices?
Bibliographic record
Abstract
OBJECTIVE: To determine which interventions are effective in influencing health practitioners' prescribing practices and explore differences in intervention complexity, setting, sustainability, cost effectiveness, and impact on patient outcomes. METHODS: A systematic search for English-language systematic reviews was performed in MEDLINE, Cumulative Index of Nursing and Allied Health Literature, EMBASE, and the Cochrane Library from the date of inception to July 2005 using search terms in accordance with Cochrane recommendations. Included reviews were required to clearly report a search strategy, inclusion/exclusion criteria, literature assessment criteria, and methods for synthesizing or summarizing information and references. Two reviewers independently identified studies for inclusion, assessed study quality, and extracted relevant information. Interventions were classified as consistently effective, inconsistently effective, and effectiveness uncertain. RESULTS: Thirty-four of 4585 titles reviewed met the inclusion criteria. Quality scores ranged from 70% to 100%. Consistently effective interventions included reminders (manual and computerized), audit and feedback, educational outreach visits, organizational strategies, and patient-mediated interventions. Inconsistently effective interventions included computer decision support systems and educational meetings. Multi-faceted interventions were consistently shown to be more efficacious than single interventions. Limited data precluded exploration of the effects of interventions in different settings, sustainability of effect, cost effectiveness, and patient clinical outcomes. CONCLUSIONS: Interventions that are most effective for impacting prescribing practice include audit and feedback, reminders, educational outreach visits, and patient-mediated interventions. To maximize impact, pharmacists' efforts to positively impact prescribing practices should focus on these intervention types rather than relying primarily on passive didactics or dissemination of guidelines.
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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 | 0.037 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".