Understanding the Treatment Disparities in Canadian College Students Experiencing Mental Health Problems: A Regression Analysis
Bibliographic record
Abstract
PURPOSE: Investigate the sociodemographic disparities in mental health treatment among Canadian college students through quantifying disparities at 2 critical stages of the treatment-seeking pathway: (1) perceived need for treatment and (2) service use, conditional on perceived need. METHODS: Survey data collected at 4 Canadian universities under the World Mental Health International College Student initiative were analyzed. Students meeting the criteria for 12-month mental disorder, substance use disorder, suicidal thoughts and behaviors, and/or non-suicidal self-injury were included in the analyses (N = 8,581). Multivariable logistic regression models were run to evaluate the associations between sociodemographic characteristics and (1) perceived need for mental health treatment and (2) service use, conditional on perceived need. Gender, sexual orientation, race and ethnicity, age, international student status, parental education, and financial stress were investigated as covariates. RESULTS: Students meeting the criteria for a 12-month mental health condition comprised 30.4% of the total sample. Of these students, 78.7% reported a perceived need for mental health treatment and 40.2% reported service use. Gender and sexual orientation disparities appeared to predominately arise from differences in perceived need. Conversely, disparities based on age, international student status, and financial stress appeared to predominately arise from differences in structural barriers. Disparities based on race/ethnicity and parental education appeared to arise from both attitudinal and structural barriers. DISCUSSION: Findings identify key blockage points for further investigation and highlight heterogeneity in the causes underlying sociodemographic disparities in service use, emphasizing the importance of targeted efforts to promote equitable access to mental health treatment for students.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".