Major depression: prevalence, treatment utilization and age in Canada.
Bibliographic record
Abstract
OBJECTIVE: To determine the relationships between age, prevalence of major depression and antidepressant treatment in Canadian populations by using data from three health surveys. BACKGROUND: Major depression is an important disorder, and there is a concern that treatment for it may be underutilized. Age may have an impact on treatment use. METHODS: Two of the surveys incorporated into the present analysis were random digit dial telephone surveys, with sample sizes of 2542 subjects and 796 subjects, respectively. The third data source was the National Population Health Survey (n=73,402), which used some face to face and some telephone-based data collection methods. In each of these studies, major depression was evaluated using a short form version of the Composite International Diagnostic Interview for Major Depression. Diagnostic status was evaluated in relation to current use of antidepressant medications, age and other relevant factors. All analyses accounted for unequal selection probabilities and clustering inherent in the sampling procedures. RESULTS: The prevalence of major depression was consistently found to be highest in subjects under the age of 45 years. However, older subjects were more likely to report having depression diagnosed. Antidepressant treatment was found to be less frequent in young subjects, despite these subjects being in the group with the highest prevalence of depression. Younger respondents were less likely to see a physician about their depressive symptoms, and when they did they were less likely to be told that they had a depressive disorder. CONCLUSIONS: The data presented indicate that antidepressant medications tend to be used less frequently in younger than older age groups in Canada. Additional studies are needed to confirm optimal use rates in relation to age.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".