The influence of prescriber and patient gender on the prescription of benzodiazepines: results from the Florida Medicaid Dataset
Bibliographic record
Abstract
BACKGROUND: Benzodiazepine (BZD) prescription rates have increased over the past decade in the United States. Available literature indicates that sociodemographic factors may influence diagnostic patterns and/or prescription behaviour. Herein, the aim of this study is to determine whether the gender of the prescriber and/or patient influences BZD prescription. METHODS: Cross-sectional study using data from the Florida Medicaid Managed Medical Assistance Program from January 1, 2018 to December 31, 2018. Eligible recipients ages 18 to 64, inclusive, enrolled in the Florida Medicaid plan for at least 1 day, and were dually eligible. Recipients either had a serious mental illness (SMI), or non-SMI and anxiety. RESULTS: Total 125 463 cases were identified (i.e., received BZD or non-BZD prescription). Main effect of patient and prescriber gender was significant F(1, 125 459) = 0.105, P = 0 .745, partial η2 < 0.001. Relative risk (RR) of male prescribers prescribing a BZD compared to female prescribers was 1.540, 95% confidence intervals (CI) [1.513, 1.567], whereas the RR of male patients being prescribed a BZD compared to female patients was 1.16, 95% CI [1.14, 1.18]. Main effects of patient and prescriber gender were statistically significant F(1, 125 459) = 188.232, P < 0.001, partial η2 = 0.001 and F(1, 125 459) = 349.704, P < 0.001, partial η2 = 0.013, respectively. CONCLUSIONS: Male prescribers are more likely to prescribe BZDs, and male patients are more likely to receive BZDs. Further studies are required to characterize factors that influence this gender-by-gender interaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".