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Record W2137981068 · doi:10.7870/cjcmh-2013-023

Does Youth Net Decrease Mental Illness Stigma in High School Students?

2013· article· en· W2137981068 on OpenAlexaffvenue
Linda O’Mara, Noori Akhtar‐Danesh, Gina Browne, D.R. Mueller, Lorraine Grypstra, Cheryl Vrkljan, Malcolm Powell

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2013
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsCentre for Addiction and Mental HealthHamilton Health SciencesMinistry of Health and Long Term CareMcMaster University
Fundersnot available
KeywordsStigma (botany)Mental healthMental illnessPromotion (chess)PsychologyIntervention (counseling)Clinical psychologyDepression (economics)Health promotionRandomized controlled trialPsychiatryMedicineNursingPublic health

Abstract

fetched live from OpenAlex

This study examined whether stigma toward mental illness decreased for youth after participating in focus groups in a school-based mental health promotion program called Youth Net (YN FGs). A total of 294 students from 6 high schools participated in a randomized controlled trial, completing questionnaires that measured stigma and depression. Stigma decreased for participants in the intervention group in low-need schools only. Study findings suggest continuing with YN FGs in low-need schools and working collaboratively with community partners to provide evidence of the effectiveness of mental health promotion with youth.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.366
Teacher spread0.328 · 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 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

Citations17
Published2013
Admission routes2
Has abstractyes

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