Policies for Deprescribing: An International Scan of Intended and Unintended Outcomes of Limiting Sedative-Hypnotic Use in Community-Dwelling Older Adults
Why this work is in the frame
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Bibliographic record
Abstract
Policies have been put in place internationally to reduce the overuse of certain medications that have a high risk of harm, such as sedative-hypnotic drugs for insomnia or opioids for chronic non-cancer pain. We explore and compare the outcomes of policies aimed at deprescribing sedative-hypnotic medication in community-dwelling older adults. Prescription monitoring policies led to the highest rate of discontinuation but triggered inappropriate substitutions. Financial deterrents through insurance scheme delistings increased patient out-of-pocket spending and had minimal impact. Pay-for-performance incentives to prescribers proved ineffective. Rescheduling alprazolam to a controlled substance raised the street drug price of the drug and shifted use to other benzodiazepines, causing similar rates of overdose deaths. Driving safety policies and jurisdiction-wide educational campaigns promoting non-drug alternatives appear most promising for achieving intended outcomes and avoiding unintended harms. Sustainable change should be supported with direct-to-patient education and improved access to non-drug therapy, with an emphasis on evaluating both intended and unintended consequences of any deprescribing-oriented policy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it