Limits to Stepped Care 2.0 for LGBTQ+ Students at a Major University in Ontario, Canada
Bibliographic record
Abstract
In recent years, postsecondary institutions have experienced increasing demands for on-campus mental health care services as rates of mental health concerns continue to grow amongst the student population, particularly amongst marginalized groups of students, including lesbian, gay, bisexual, trans, and queer (LGBTQ+) students. Amidst claims to resource scarcity within the context of postsecondary mental health care, the Stepped Care 2.0 framework for organizing mental health care service delivery has emerged as an attractive methodology for (re)structuring postsecondary mental health care services, as the model claims to increase access to a diversity of services. Writing in the context of a large postsecondary institution in Ontario, the present qualitative study uses the critical qualitative methodology of institutional ethnography to examine how LGBTQ+ students experience care as organized by Stepped Care 2.0 model. Ultimately, our paper points to disjunctures between Stepped Care 2.0’s self-promotional claims to accessibility and the lived experiences of LGBTQ+ students, who have overwhelmingly reported that on-campus mental health care services have been difficult to access. Using qualitative interviews and textual analysis, we argue that LGBTQ+ students’ difficulties with accessing the care they need can be attributed to the structure of the Stepped Care 2.0 model itself, namely its focus on One-At-A-Time, short-term therapy. Our paper aims to be critically additive to the existing body of research on Stepped Care 2.0 and ends with a discussion of the implication of our work for academic literature, policy, and practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.026 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".