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Record W7132886335

Support Seeking on Campus: The Unique Support-seeking Experiences of Racialized University Students

2023· dissertation· W7132886335 on OpenAlexaffabout
Rya Buckley

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsMental healthIntersectionalityThematic analysisReflexivityTheme (computing)Qualitative researchWhite (mutation)
DOInot available

Abstract

fetched live from OpenAlex

Despite experiencing greater rates of mental health challenges, racialized postsecondary students are accessing campus-based supports at lower rates than their White peers. As much of the existing research pertaining to racialized students’ support-seeking at postsecondary institutions has focused on the barriers to access, little is known about the experiences of racialized students who overcome these barriers and access support. In the present study, the unique experiences of racialized students accessing wellness and mental health supports on a large university campus in Canada were examined. Thirty-one racialized undergraduate students completed a demographic questionnaire and semi-structured interview, which was analyzed using reflexive thematic analysis with intersectionality as a theoretical framework. Five themes were developed under the overarching theme that Racialized students are ostracized by a mental health system built without them in mind. This study has implications for creating more inclusive mental health supports on postsecondary campuses.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0110.003
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.443
Teacher spread0.396 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes2
Has abstractyes

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