Depression Severity and Its Predictors: Findings from a Nationally Representative Canadian Sample
Bibliographic record
Abstract
Depression is a major global health issue that significantly contributes to the burden of disease. Despite the wealth of existing research on depression, several key aspects remain underexplored, including factors that predict the onset, severity, and recurrence of depressive symptoms. The purpose of the current study was to assess the sociodemographic correlates and risk and protective factors of depression using a representative sample of the Canadian population. The data were drawn from the 2017–2018 Canadian Community Health Survey (CCHS), a cross-sectional survey with a sample size greater than 113,000. Results from regression analyses identified sleep quality, social support, and perceived life satisfaction as protective factors for depression severity, while a current, self-reported diagnosis of an anxiety- or mood-related disorder was identified as a risk factor. Being younger emerged as the only pertinent sociodemographic risk factor for depression. Contrary to expectations, vigorous physical activity and sedentary behaviour did not significantly predict depression severity. Taken together, the results underscore the importance of identifying modifiable risk and protective factors to inform population-level mental health strategies (e.g., campaigns seeking to raise awareness regarding the importance of sleep, social support) to guide the development of targeted, evidence-based interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".