Is Mental Health in the Canadian Population Changing over Time?
Bibliographic record
Abstract
OBJECTIVE: Mental health in populations may be deteriorating, or it may be improving, but there is little direct evidence to support either possibility. Our objective was to examine secular trends in mental health indicators from national data sources. METHODS: We used data (1994-2008) from the National Population Health Survey and from a series of cross-sectional studies (Canadian Community Health Survey) conducted in 2001, 2003, 2005, and 2007. We calculated population-weighted proportions and also generated sex-specific, age-standardized estimates of major depressive episode prevalence, distress, professionally diagnosed mood disorders, antidepressant use, self-rated perceived mental health, and self-rated stress. RESULTS: Major depression prevalence did not change over time. No changes in the frequency of severe distress were seen. However, there were increases in reported diagnoses of mood disorders and an increasing proportion of the population reported that they were taking antidepressants. The proportion of the population reporting that their life was extremely stressful decreased, but the proportion reporting poor mental health did not change. CONCLUSIONS: Measures based on assessment of symptoms showed no evidence of change over time. However, the frequency of diagnosis and treatment appears to be increasing and perceptions of extreme stress are decreasing. These changes probably reflect changes in diagnostic practice, mental health literacy, or willingness to report mental health concerns. However, no direct evidence of changing mental health status was found.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".