Beyond Pathways to Care: Exploring the Role of Boundaries in Mental Health
Bibliographic record
Abstract
The Pathways to Care model is an increasingly popular method of healthcare delivery in institutional settings like higher education. Pathways are taken for granted as linear trajectories of care that are intuitive to navigate. However, care is often messy, diverse, and counterintuitive in practice. Set within the context of a Canadian university, students fill gaps generated by inadequate institutional Pathways to Care through self-care. Namely, students take up boundary-making as a generative and relational form of self-caring. Methods include social cartography and narrative accounting of care pathways by students supplemented by interviews with campus mental health stakeholders and providers. Results demonstrate that both pathways and boundaries can be limiting and potentiating in people’s search for support. Boundaries mediate emotional proximity and distance—or emotional emplacement—and in doing so generate new forms of intimacy, support, and healing. I advance theoretical conversations on the emplaced nature of care by documenting the role of self-care in people’s care journeys. I also forward social cartography as a fruitful avenue through which to understand the complexities of subjective experiences with care. These contributions amplify the voices of lived experience in understanding mental well-being 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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.066 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.006 |
| 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".