Factors associated with mental illness among individuals experiencing socioeconomic disadvantages in Edmonton, Canada: an exploratory analytical study
Bibliographic record
Abstract
In Canada, over 5 million people experienced mental illness in 2022; individuals in the lowest income quintile were more likely to have unmet mental healthcare needs. This study utilized a cross-sectional design and purposive sampling to explore factors associated with mental illnesses among individuals experiencing socioeconomic disadvantages in Edmonton, Canada. Quantitative data were collected through an interviewer-administered questionnaire. Logistic regression, Chi-square, and Fischer’s Exact tests explored associations between independent variables and mental illness. Of 392 participants recruited from five organizations, 39.03% reported unstable housing, 35.46% consumed tobacco, alcohol, or other recreational substances, and 27.04% reported having a diagnosed mental illness. Being a woman was associated with more than 3 times greater odds of mental illness compared to men (OR = 3.12, 95%CI: 1.74–5.58, p-value: 0.0001). Housing instability (OR = 1.97, 95%CI: 1.12–3.44, p-value: 0.018), food insecurity (OR = 3.01, 95%CI: 1.33–6.82, p-value: 0.008), financial barriers to accessing healthcare (OR = 2.56, 95%CI: 1.46–4.49, p-value: 0.001), tobacco use (OR = 2.69, 95%CI: 1.51–4.79, p-value: 0.001), and recreational substance use (OR = 2.52, 95%CI: 1.39–4.57, p-value: 0.002) were associated with greater odds of mental illness. Initiatives targeting populations at higher risk of mental illnesses should address social and behavioural determinants to reduce inequities. Removing financial or operational barriers to mental health services should be a priority.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".