Does Physicians' Clinical Competence, Communication Ability, or Cultural Background Influence Potentially Inappropriate Prescribing of Benzodiazepines and Z‐Drugs Among Older Adults With Insomnia?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to estimate the association between physician's age, sex, clinical and communication competencies, and cultural background on benzodiazepines and Z-drugs (BDZ) prescribing to older adults with insomnia. METHODS: A cohort of international medical graduates (IMGs) who completed their pre-residency licensure exam in 1998-2004 were linked to all U.S. Medicare patients they provided care to in 2014-2015. Their care records in Parts A, B, and D from all physicians were extracted. The first outpatient visit for insomnia to a study IMG was identified for each patient in that period. The outcome was incident BDZ prescribing by the study physician following the visit. Main exposures were physician age, sex, citizenship at birth, and clinical and communication competency as measured on the licensure exam. The association between physician characteristics and BDZ prescribing, adjusting for physician and patient covariates, was estimated using generalized estimating equations multivariable logistic regression. RESULTS: We analyzed 28 018 patients seen by 4069 unique physicians. IMGs born in all other regions of the world were less likely to prescribe BDZs compared to U.S.-born IMGs, with physicians from the United Kingdom being least likely (OR 0.54 [95%CI 0.34-0.85]). Neither physician's clinical competency nor communication ability were associated with BDZ prescribing (OR per 10% increase, respectively: 0.95 [95%CI 0.88-1.02] and 0.98 [95%CI 0.93-1.04]). Older physicians remain more likely to prescribe BDZ (OR per 5-year increase 1.04 [95%CI 1.00-1.08]). CONCLUSIONS: The associations between cultural background and physician's age on BDZ prescribing highlight the potential targets for remedial solutions to reduce the use of potentially inappropriate medications.
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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 | 0.001 | 0.007 |
| 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) | 0.001 | 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".