The Prevalence of Major Depressive Episodes Is Higher in Urban Regions of Canada
Bibliographic record
Abstract
OBJECTIVE: Major depressive disorder is an important contributor to disease burden. Anticipation of service needs is important, yet basic information is lacking. For example, there is no consensus as to whether major depressive episodes (MDE) are more or less prevalent in urban or rural areas. The objective of this study was to determine whether a difference exists in Canada. METHOD: A series of 11 Canadian national cross-sectional studies were examined from 2000 to 2014, providing much greater precision than prior analyses. Survey-specific MDE prevalence estimates were synthesized into a pooled odds ratio comparing urban to rural areas using meta-analytic methods. RESULTS: Differences in the survey-specific estimates were not in excess of what would be expected due to sampling variability. This suggests that inconsistency in the prior literature is due to inadequate power and precision, an issue addressed by the meta-analytic pooling. The pooled odds ratio for Canada is 1.18 (95% confidence interval, 1.12 to 1.25), indicating that urban regions have higher MDE prevalence than rural regions. However, the difference is very small and of uncertain significance for policy and planning. CONCLUSIONS: Prevalence of MDE is approximately 18% higher in urban compared to rural regions of Canada. The difference is insufficient to impute differing need for services, but the result resolves an inconsistency in the existing literature and may play a role in future needs assessment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".