Mental Health Service Type Use and Depressive Symptoms: A Multivariable Analysis of Sociodemographic Correlates: Utilisation des services de santé mentale et symptômes dépressifs : Analyse multi-variables des corrélats sociodémographiques
Bibliographic record
Abstract
Objective Public and private mental health-funded services differ in terms of accessibility, affordability, and perceived quality, potentially impacting outcomes. Understanding how different mental health service types and sociodemographic factors correlate with depressive symptoms is critical for informing equitable mental health policies and resource allocation. This study investigates the associations between type of mental health service used and depressive symptoms. Method Data from Mental Health Research Canada's National Poll Understanding the Mental Health of Canadians 2022 to 2024 was analyzed. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9). Mental health-funded services were classified as public or private. Linear and logistic regressions examined associations with depressive symptom severity (total PHQ-9 scores) and presence of depressive symptoms (PHQ-9 score ≥ 10). Mediation analysis was conducted to explore the mediating effects of household income on the relationship between funded service type and depressive symptoms. Results The study included 2,072 adults, with 1,000 (47.4%) reporting depressive symptoms. Compared to public services, individuals using private services ( n = 880, 41.8%) had lower PHQ-9 scores (aCoef: −1.34, 95% CI [−1.97, −0.70]; p < .001) and lower odds of having depressive symptoms (aOR: 0.74, 95% CI [0.60, 0.91]; p = .004). Household income partially mediated the relationship between funded service type and PHQ-9 scores, accounting for 39.5% of the effect ( p < .001), and fully mediated the relationship with the presence of depressive symptoms, with a mediation effect of 40.6%. Additionally, individuals who attended services monthly, weekly, or more frequently had higher odds of having depressive symptoms (aOR: 2.86, 95% CI [1.23, 6.68]; p = .015). Conclusion This study highlights the complex interplay between mental health service types used, sociodemographic factors, and mental health outcomes. These insights underscore the need to address barriers to effective mental health care access and tailor interventions to individuals’ socioeconomic and demographic contexts to optimize outcomes. Plain Language Summary Title How Public and Private Mental Health Services Relate to Depression in Canada
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".