Changing Perceptions of Mental Health in Canada
Bibliographic record
Abstract
OBJECTIVE: Epidemiologic studies typically assess mental health using diagnostic measures or symptom severity measures. However, perceptions are also important. The objective of our study was to evaluate trends in perceived mental health in Canada during the past 20 years using data collected in a series of surveys. METHOD: Perceived mental health status, the stressfulness of most days, and perceived general health, have been repeatedly measured in national surveys. In our study, the resulting frequencies and 95% confidence intervals were calculated. Distress was also assessed in the same surveys with the Kessler 6 Psychological Distress Scale, and analyzed using mean scores and frequencies based on cut-points. Data synthesis used forest plots. Time trends were assessed using random effects meta-regression models. RESULTS: No detectable changes in distress were found. Similarly, self-rated general health remained stable. However, over time, Canadians became slightly more likely to report that their mental health was merely fair or poor. Conversely, they have been progressively less likely to perceive that their lives are quite a bit or extremely stressful. CONCLUSION: While these observations are ecological, the 2 trends may be related: distressing emotional experiences may increasingly be interpreted as evidence of a disturbance of mental health rather than a reaction to stressful circumstances. These changing perceptions should not be misinterpreted as an epidemic of poor mental health.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".