The intended and unintended consequences of benzodiazepine monitoring programmes: a review of the literature
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: Concern has been expressed regarding the potential over-prescription of benzodiazepines (BZDs) and their potential for misuse and abuse. Patterns of BZD use can be tracked by prescription monitoring programmes (PMPs). This study reviews the literature examining the impact of PMPs on the use of BZDs. METHODS: Studies published in English from January 1980 to April 2009 were identified though PubMed, EMBASE, IPA, CINHL and Web of Science using MeSH terms: 'Benzodiazepines' OR 'Benzodiazepines/supply and distribution' AND ('Social Control, Formal/legislation, jurisprudence'); Emtree terms: 'drug control'/exp AND 'benzodiazepine derivative'/exp/mj. A broad search strategy was also used: benzodiazepines; triplicate prescription program; prescription monitoring program; triplicate prescribing; and triplicate prescription policy. RESULTS AND DISCUSSION: This search identified 32 relevant articles that addressed the impact of implementation of a PMP for BZDs in New York State in 1989. Overall, BZD prescribing declined following implementation, but the decline was not consistent across population groups. In particular, marginalized and vulnerable populations, such as persons with chronic mental health disorders, may be disproportionately affected. WHAT IS NEW AND CONCLUSION: We provide a critical review of the impact of PMPs on the use of BZDs. PM decreases overall use of BZDs, but may have unintended consequences that differentially impact certain populations. Furthermore, research is warranted to understand better the long-term costs and benefits.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".