Patterns of benzodiazepine use in a Canadian population sample
Bibliographic record
Abstract
AIM: The objective of this study was to identify clinical and demographic factors that may be associated with benzodiazepine treatment, to describe the reported reasons for use of these medications and to appraise the pattern of use in relation to standard guidelines in a general population sample. METHODS: Telephone survey methods were employed to select a sample of 3345 people between the ages of 18 and 64. A computer assisted telephone interview, including the Mini Neuropsychiatric Diagnostic Interview (MINI), was administered. Estimates were weighted for design features and population demographics. RESULTS: The overall prevalence of benzodiazepines use was 3.3% (95% confidence interval [CI] 2.6 to 4.1%). There was a higher frequency of medication use in women than men, among respondents who were widowed, separated or divorced, and those with lower levels of education. In relation to MINI diagnosis, diagnoses of Panic Disorder and Major Depression increased the probability of taking benzodiazepines. The reported main reason for use was "Sleep disorders" (68.9%), "Anxiety" (35.8%), "Depression" (27.8%) and "Pain management" (21.2%). More than 80% of subjects were taking benzodiazepines for more than one year. CONCLUSIONS: When compared to previous estimates, the lower frequency of benzodiazepines use suggests that there has been improvement in their evidence-based use at a population level. However our results once more confirm the difficulty stopping the use of these medications once they have been started. Further randomized control studies may help clinicians in having a better practical approach to rational benzodiazepine use.
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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 | 0.001 | 0.002 |
| 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 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".