Factors influencing the effects of policies and interventions to promote the appropriate use of medicines in high-income countries: A rapid realist review
Bibliographic record
Abstract
BACKGROUND: The appropriate use of medicines has long been recognized as a fundamental component of medicine policies. We aimed to extract lessons from published research on how policy contexts and mechanisms can affect the outcomes of national- or health-system level interventions to promote appropriate medicine use (defined as an increase in underutilized medications or decrease in inappropriate medication use). METHODS: We conducted a rapid realist review of published evidence concerning system-level policies to promote the appropriate use of medicines in high-income countries with universal prescription drug coverage. We searched MEDLINE and Embase to identify relevant publications. We used a realist evaluation framework to identify contexts, mechanisms, and outcomes for each intervention and to hypothesize which policy contexts and mechanisms supported successful outcomes in terms of relative changes in the prevalence of use of the specific medication classes targeted. RESULTS: From 1,318 identified studies, 18 met our inclusion criteria. 13 distinct policies were identified. Three main policy-related factors underpinned successful interventions: involving providers and patients through program interventions; central coordination through national agencies dedicated to medicine policies; and the establishment of an explicit and integrated national medicine policy strategy. CONCLUSION: Policymakers can improve coordination of national pharmaceutical policies to reduce harms from inappropriate medicines use, thus improving health outcomes through cost-effective programs.
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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 | 0.065 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".