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Record W2985865217 · doi:10.1080/07448481.2019.1682002

Correlates of explicit and implicit stigmatizing attitudes of Canadian undergraduate university students toward mental illness: A cross-sectional study

2019· article· en· W2985865217 on OpenAlexafffundabout
Harman Singh Sandhu, Anish Arora, Jennifer Brasch

Bibliographic record

VenueJournal of American College Health · 2019
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcMaster UniversityMcGill UniversityImpact
FundersMcMaster University
KeywordsMental illnessStigma (botany)DemographicsPsychologyImplicit-association testClinical psychologyMental healthTest (biology)Association (psychology)Cross-sectional studySocial stigmaSocial distancePsychiatryMedicineSocial psychologyFamily medicineDiseaseDemographyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Objective To assess explicit and implicit attitudes toward mental illness of undergraduate students and explore associated variables. Participants: Year 1–4 undergraduate students from a large Canadian university (n = 382). Methods: Participants completed demographics, the Opening Minds Scale for Healthcare Providers, and an Implicit Association Test. Two-tailed independent and paired-samples t-tests, and ANOVA were performed with significance level at p < .05. Results: About 67.5% self-reported having experienced a mental illness and 31.2% had been diagnosed. Lower explicit stigma was associated with females, those with a history of mental illness diagnosis, and those who have had a close relationship with someone experiencing a mental illness. Faculty of Social Sciences students had significantly lower explicit stigma scores than Faculty of Engineering students. Implicit stigma did not show significant associations with any factors. Conclusions: A high proportion of undergraduate students experience mental illness. Increased exposure and experience were associated with reduced explicit stigma.

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)0.001
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 score0.093
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.377
Teacher spread0.343 · 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

Citations14
Published2019
Admission routes3
Has abstractyes

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