Bibliographic record
Abstract
Recommendations for benzodiazepine (BZD) use suggest durations of no more than a few weeks, but studies report use for months, years, or even decades. This article examines the who (who are long-term users), why (why do they use BZD), what (what are patterns of long-term use) and how (how do they compare to all BZD users). The study population is from the National Population Health Survey in Canada which interviewed respondents four times at two-year intervals, asking about specific drugs use as well as demographic, lifestyle and health-related questions. Long-term BZD use was defined as BZD use for two successive cycles. Four percent of the Canadian population used BZD at any one time, half of whom also reported use in the previous cycle. Benzodiazepine users were more likely to be female, elderly, smokers, to prefer speaking a language other than English, to have insurance coverage for medication, and to have completed high school education. Almost none of these determinants predicted long-term use. Persons reporting BZD use in 2000 had an odds ratio (OR) of 38.6 for also using BZD in 1998, were more likely to use antidepressants (OR=8.5) and suffer from conditions such as poor health, stress, and pain. Most of these determinants had no association with long-term use or if they did at a considerably lower OR. Of the 395 BZD users in 2000, almost 48.4% also used BZD in the previous cycle and 17% in all three previous cycles. Benzodiazepine use in any previous cycle made BZD use in 2000 more likely, with use determined by how recent and the frequency of reported use, culminating in a very high OR of 83.3 for use in all three previous cycles. Continued use for any of the individual BZD tended to be largely for the same BZD. We conclude that: (1) the overriding determinant for BZD use was that of previous use; and (2) long-term use was not determined by the same factors as overall use, which is significant in developing approaches to dealing with long-term BZD use.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".