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Beyond Pathways to Care: Exploring the Role of Boundaries in Mental Health

2025· article· en· W4416939345 on OpenAlexaffvenueabout
Loa Gordon

Bibliographic record

VenueAnthropologica · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCounterintuitiveMental healthSet (abstract data type)LimitingContext (archaeology)NarrativeMental health careHealth care

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.170
GPT teacher head0.434
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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