Prevalence of psychological distress: How do Australia and Canada compare?
Bibliographic record
Abstract
OBJECTIVE: To compare equivalent population-level mental health indicators in Canada and Australia, and articulate recommendations to support equitable mental health services. These are two somewhat similar resource-rich countries characterized by extensive non-metropolitan and rural regions as well as significant areas of socioeconomic deprivation. METHODS: A cross-national epidemiology and equity study: primary outcome was Kessler Psychological Distress Scale (K10) in recent national surveys. A secondary outcome was mental disorders rate since these surveys were 5-years apart. RESULTS: Elevated distress, defined by K10 scores (0-40 range) of 12 and over, affected 11.1% Australians and 12.0% Canadians. Elevated distress in both countries affected more people in the lowest income quintile (21-27%) compared to the richest (6%). In the lowest income quintile, 1-in-4 Australians and 1-in-5 Canadians reported elevated distress - twice the national average in both countries. Australians in the lowest income quintile (over 5 million people) have a significantly higher risk by over a 5% for elevated distress compared to their low-income Canadian counterparts. After adjusting for effects of age and gender, the relative odds in the lowest quintile compared to richest was 6.4 for Australians and 3.5 for Canadians, which remained significantly different thus confirming greater inequity in Australia. Mental disorders affected approximately 1-in-10 people in both countries. CONCLUSIONS: This adds to the mental health prevalence monitoring in these two countries by supporting an overall prevalence of elevated distress in approximately 1-in-10 people. It supports large-scale public health interventions that target elevated distress in people with low incomes to order to achieve the biggest impact, and, to reduce the greater inequity in mental health indicators in Australians, policy-makers should consider eliminating gap-fees as they are illegal in Canada. As encouraged by World Health Organization, we highlight the importance of such population-level studies so that cross-national results can be reliably compared.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".