Determinants of Coprescription of Anxiolytics with Antidepressants in General Practice
Bibliographic record
Abstract
OBJECTIVES: Anxiolytics are the most frequently prescribed psychotropic drugs in France. General practitioners (GPs) tend to prescribe anxiolytics and other benzodiazepines to patients with major depressive disorders (MDD). However, the extent to and reasons for which they prescribe these medications together are not well documented. This article assesses how often and why GPs coprescribe an anxiolytic when starting patients on antidepressant (AD) treatment, and which patient- and GP-related factors are associated with this coprescription. METHODS: We used a survey of 131 GPs practising in southeastern France and of patients seen consecutively during June to October 2004 to whom they prescribed an AD. Data were collected from GPs (consultation-questionnaires focusing on their prescription, diagnosis, and symptom detection) and patients (self-administered questionnaires, including the Hospital Anxiety and Depression scale, and social and demographic characteristics). Factors associated with anxiolytic coprescription were analyzed with a multilevel logistic regression. RESULTS: GPs completed 438 consultation-questionnaires for patients; 258 patients (58.9%) returned their questionnaires. Sixty percent of the patients received anxiolytics with ADs. Anxiolytics were prescribed more frequently by male GPs who reported feeling ill at ease treating MDD, or detected suicidal ideation or anxiety in their patients, and finally to patients with stable jobs. CONCLUSIONS: Although some practice guidelines and authors acknowledge that there might be some justification for coprescribing anxiolytics with ADs at the beginning of MDD treatment in specific situations, the high percentage of coprescriptions for anxiolytics observed in our study suggests that training and knowledge of GPs about MDD treatment are not optimal.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".