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Record W2100747122 · doi:10.1177/070674370404900309

The Feasibility of a Mental Health Curriculum in Elementary School

2004· article· en· W2100747122 on OpenAlexafffundvenue
Bianca Lauria-Horner, Stan Kutcher, Sarah J. Brooks

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2004
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsDalhousie University
FundersHealth Canada
KeywordsCurriculumMental healthPsychological interventionAnxietyMedical educationPsychologyMental health literacyMedicinePsychiatryMental illnessPedagogy

Abstract

fetched live from OpenAlex

OBJECTIVE: To establish the feasibility and short-term impact of implementing a novel curriculum in a linguistically and geographically isolated francophone community to enhance elementary schoolchildren's (Grades 1 to 7; n = 158) knowledge and attitudes regarding mental health. METHODS: The project team developed a curriculum that covered expected emotional development, depression, anxiety disorders, and attention-deficit hyperactivity disorder to be delivered by the school's usual teachers. Committee members led focused discussions (Grades 1 to 7) and administered evaluation questionnaires (Grades 4 to 7) surveying students' knowledge and attitudes before and after implementation. RESULTS: Teachers were enthusiastic about the project. Parents were initially skeptical, but post hoc interventions by school staff secured participation consent for 98% of the students. Baseline data (Grades 4 to 7) revealed little knowledge and some negative attitudes regarding mental illnesses; postprogram data indicated improved knowledge and suggested improved attitudes. CONCLUSIONS: The project was made feasible by the high degree of involvement of local community members. Children's (Grades 4 to 7) mental health awareness and understanding was enhanced by the curriculum. Effects on help-seeking behaviour and case identification have yet to be assessed.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.425
Teacher spread0.382 · 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

Citations30
Published2004
Admission routes3
Has abstractyes

Explore more

Same venueThe Canadian Journal of PsychiatrySame topicSchool Health and Nursing EducationFrench-language works237,207