A comparison of antidepressant use in Nova Scotia, Canada and Australia
Bibliographic record
Abstract
PURPOSE: The prevalence of major depression is reported as approximately 8% in Canada and 7.5% in Australia, the use of antidepressants is therefore common. However, questions remain about whether depression is under-diagnosed and whether patients are appropriately treated with antidepressants once the disorder is recognized. We compared the use of antidepressant medicines, in Nova Scotia, Canada and Australia, in populations receiving public drug subsidy. METHODS: The Nova Scotia Pharmacare Program and the Pharmaceutical Benefits Scheme in Australia were used to obtain dispensing data for all publicly subsidized antidepressants. Utilization was compared from 2000-2003, using the World Health Organisation Anatomic Therapeutic Chemical (ATC)/Defined Daily Dose (DDD) system. RESULTS: The use of antidepressants increased in both areas over the study period. However, the use of antidepressants in Nova Scotia increased at a significantly higher rate than Australia. Selective serotonin reuptake inhibitors (SSRIs) were the most commonly prescribed class of drugs in both areas, constituting 60% of all antidepressants prescribed. Eight different antidepressants made up 90% of the antidepressant drug use in Australia, with sertraline the most commonly prescribed. Similarly, nine different antidepressants made up 90% of the antidepressant use in Nova Scotia, with paroxetine most commonly prescribed. CONCLUSIONS: This study found differences in the rate but not class of antidepressant prescribing in Nova Scotia and Australia. Antidepressant use increased in both areas over the time period. This may be due to increased exposure to marketing, promotion, education or different prescribing practices in Nova Scotia compared to Australia.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".