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Record W2498713164 · doi:10.1111/acps.12613

Reducing stigma in high school youth

2016· article· en· W2498713164 on OpenAlexafffundabout
Michelle Koller, Heather Stuart

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2016
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsQueen's University
FundersHealth Canada
KeywordsStigma (botany)PsychologySocial stigmaPsychiatryClinical psychologyDevelopmental psychologyMedicineFamily medicineHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

OBJECTIVE: We evaluated 21 contact-based education interventions in 5047 Canadian high school students and identified student characteristics associated with success. METHODS: We used a one-group pretest/posttest design with standardized instruments to measure changes in behavioural intent. Variability across interventions was assessed using meta-analysis, and a mixed-effects logistic regression was used to identify student characteristics. RESULTS: Interventions were heterogeneous (I(2) = 62.4%) but generally successful. The odds of getting an A grade was 2.57 times greater on the posttest than the pretest (95% CI = 2.18, 3.03). Males were less likely to achieve a passing score overall; however, males who self-disclosed a mental illness were more likely to pass. Three percent of students experienced a large drop in social acceptance following the intervention. These were more likely to be male [OR = 1.5 (95% CI = 1.0, 2.1)]. CONCLUSION: Contact-based education is a promising practice for reducing stigma in high school students, although the field would benefit from fidelity criteria to reduce variation across interventions. Males and females react differently to antistigma programming; particularly those with self-reported mental illnesses and a small proportion may become more intolerant.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.330
Teacher spread0.305 · 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; both teacher heads agree on what is shown here.

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

Citations54
Published2016
Admission routes3
Has abstractyes

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