Physician benzodiazepine self-use prior to and during the COVID-19 pandemic in Ontario, Canada: a population-level cohort study
Bibliographic record
Abstract
Objectives The aim of this study was to investigate physician benzodiazepine (BZD) self-use pre-COVID-19 pandemic and to examine changes in BZD self-use during the first year of the pandemic. Design Population-based retrospective cohort study using linked routinely collected administrative health data comparing the first year of the pandemic to the period before the pandemic. Setting Province of Ontario, Canada between March 2016 and March 2021. Participants Intervention Onset of the COVID-19 pandemic in March 2020. Outcomes measures The primary outcome measure was the receipt of one or more prescriptions for BZD, which was captured via the Narcotics Monitoring System. Results In a cohort of 30 798 physicians (mean age 42, 47.8% women), we found that during the year before the pandemic, 4.4% of physicians had 1 or more BZD prescriptions. Older physicians (6.8% aged 50+ years), female physicians (5.1%) and physicians with a prior mental health (MH) diagnosis (12.4%) were more likely than younger (3.7% aged <50 years), male physicians (3.8%) and physicians without a prior MH diagnosis (2.9%) to have received 1 or more BZD prescriptions. The first year of the COVID-19 pandemic was associated with a 10.5% decrease (adjusted OR (aOR) 0.85, 95% CI: 0.80 to 0.91) in the number of physicians with 1 or more BZD prescriptions compared with the year before the pandemic. Female physicians were less likely to reduce BZD self-use (aOR female =0.90, 95% CI: 0.83 to 0.98) compared with male physicians (aOR male =0.79, 95% CI: 0.72 to 0.87, p interaction =0.046 during the pandemic. Physicians presenting with an incident MH visit had higher odds of filling a BZD prescription during COVID-19 compared with the prior year. Conclusions Physicians’ BZD prescriptions decreased during the first year of the COVID-19 pandemic in Ontario, Canada. These findings suggest that previously reported increases in mental distress and MH visits among physicians during the pandemic did not lead to greater self-use of BZDs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".