Breaking the Stigma: The Role of Mental Health Literacy in Psychological Treatment-Seeking Decisions in Canadian Post-Secondary Students
Bibliographic record
Abstract
Compared to other adult age groups, emerging adults (EA; individuals ages 18 – 29 years) have the highest prevalence of several mental health problems; however, treatment rates are particularly low. This treatment gap is concerning because untreated mental health problems are associated with worsening psychiatric symptoms, substance abuse, work/academic challenges, and substantial healthcare costs. To address this issue, this study investigated the factors that influence EA’s mental health treatment-seeking decisions. Both quantitative and qualitative survey data were collected from Canadian university students (n = 122; M age = 20.5 years) to better understand the barriers and facilitators that are involved in these decisions. We used path analyses to test a theoretical model of mental health treatment-seeking that extended the Theory of Planned Behaviour (Ajzen, 1991) to include mental health literacy (MHL) and self-stigma, given that these variables have been found to be a salient facilitator and barrier, respectively. We also analyzed participants’ qualitative written accounts of their perceived barriers and facilitators using conventional content analysis to contextualize the model. Path analysis results revealed that higher levels of MHL were associated with a reduction in self-stigma and an increase in positive attitudes toward counselling. Five broad categories were constructed from the qualitative data relating to (1) particpants’ ability to recognize their mental health problems and/or the need for treatment, (2) participants’ ability to seek mental health treatment, (3) systemic variables, (4) stigma, and (5) therapy/therapist variables. The ways in which the quantitative and qualitative results converge and diverge are discussed. This study has implications for increasing rates of mental health treatment among EA with empirically-based campaigns and strategies that target MHL.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".