The Utilization of Antidepressants and Benzodiazepines among People with Major Depression in Canada
Bibliographic record
Abstract
OBJECTIVE: Although clinical guidelines recommend monotherapy with antidepressants (ADs) for major depression, polypharmacy with benzodiazepines (BDZs) remains an issue. Risks associated with such treatments include tolerance and dependence, among others. We assessed the prevalence and determinants of AD and BDZ utilization among Canadians who experienced a major depressive episode (MDE) in the previous 12 months, and determined the association of seeing a psychiatrist on the utilization of ADs and BDZs. METHOD: Data were drawn from the 2002 Canadian Community Health Survey: Health and Well-Being, a nationally representative sample of Canadians aged 15 years and older. Descriptive statistics quantified utilization, while logistic regression identified factors associated with utilization, such as sociodemographic characteristics or type of physician seen. Sampling weights and bootstrap variance estimations were used for all analysis. RESULTS: The overall prevalence of AD and BDZ utilization was 49.3% of respondents who experienced an MDE in the past 12 months and reported AD use. Key determinants of utilization were younger age and unemployment in the past week (OR 2.6; P < 0.001). Being seen by a psychiatrist increased utilization (OR 2.5; P < 0.001), possibly because psychiatrists were seeing patients with severe depression. CONCLUSION: A large proportion of people with past-year MDEs utilized ADs and BDZs. It is unclear how much of this is appropriate given that evidence-based clinical guidelines recommend monotherapy with ADs in the treatment of major depression.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".