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Record W4383872894 · doi:10.1080/15299732.2023.2233506

THE IMPACT OF ETHNIC DISCRIMINATION AND INSTITUTIONAL BETRAYAL ON CANADIAN UNIVERSITY STUDENTS’ MENTAL HEALTH

2023· article· en· W4383872894 on OpenAlexaffabout
Andreea Tamaian, Hannah Anstey, Seint Kokokyi, Bridget Klest

Bibliographic record

VenueJournal of Trauma & Dissociation · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of ManitobaUniversity of ReginaMinistry of Community Safety and Correctional Services
Fundersnot available
KeywordsBetrayalEthnic groupPsychologyMental healthClinical psychologyIdentity (music)PsychiatrySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

The aims of this study were to understand associations among mental health symptoms, ethnic discrimination, and institutional betrayal, and explore the potential role of protective factors (e.g. ethnic identity and racial regard) in attenuating the detrimental effects of discrimination and betrayal. A total of 89 racialized Canadian university students were recruited for this study. Self-report measures investigated demographics, mental health symptoms, experiences of discrimination and institutional betrayal, racial regard, and ethnic identity. Experiencing ethnic discrimination was associated with increased symptoms of depression and PTSD, even when controlling for the buffering effects of protective factors. Marginally significant results suggested that institutional betrayal might play a role in this relationship. Experiencing ethnic discrimination is linked to significant posttraumatic consequences. Unhelpful institutional responses may further aggravate symptoms. Universities have a duty to protect victims, and prevent ethnic discrimination.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.067
GPT teacher head0.432
Teacher spread0.365 · 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.

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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

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