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Record W4297184742 · doi:10.1111/camh.12597

Long‐term impact of a mental health literacy resource applied by regular classroom teachers in a Canadian school cohort

2022· article· en· W4297184742 on OpenAlexaffabout
Yifeng Wei, Jeremy Church, Stan Kutcher

Bibliographic record

VenueChild and Adolescent Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsDalhousie UniversityCollege of Veterinarians of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsStigma (botany)CurriculumMental health literacyCohortMental healthPsychologyLiteracyMedicineMedical educationPedagogyMental illnessPsychiatry

Abstract

fetched live from OpenAlex

Application of evidence-based mental health literacy (MHL) curriculum resources by classroom teachers has been demonstrated to significantly improve knowledge and decrease stigma in the short term. AIMS: To report results that extend these positive findings for a period of one year. METHOD: In a naturalistic cohort study, 332 grade 9 students (ages 14-15) in a Canadian school district learned from an evidence-based curriculum resource (the Guide) applied by classroom teachers who trained in its use. Evaluations of knowledge and stigma were conducted before the Guide, immediately following the Guide delivery and at one-year follow-up. RESULTS: Students showed significant (p < .001) and substantial (d = 0.68 and 0.67) short-term and significant (p < .001) and substantial (d = 0.44 and 0.58) long-term improvements in knowledge and reductions in stigma. Significant stigma reduction was found among female students than male students, but no gender differences on knowledge were found at long-term follow-up. Educators showed significant and substantial short-term improvements in knowledge (p < .001; d = 1.03) and reductions in stigma (p < .05; d = 0.35). CONCLUSIONS: The Guide resource delivered by trained classroom teachers may have value in enhancing MHL outcomes for young people.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.331
Teacher spread0.321 · 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.

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

Citations23
Published2022
Admission routes2
Has abstractyes

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