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Record W4302307405 · doi:10.7870/cjcmh-2022-024

Evaluation of “Bell Let’s Talk in the Classroom”: A Guide for Improving Teachers’ Confidence in Providing Mental Health Education

2022· article· en· W4302307405 on OpenAlexafffundvenueabout
Brooke Linden, Heather Stuart, Alexandra Fortier

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsMental Health Research CanadaQueen's UniversityBell (Canada)
FundersQueen's University
KeywordsMental healthTest (biology)Medical educationPsychologySample (material)MedicineApplied psychologyPsychiatry

Abstract

fetched live from OpenAlex

The Let’s Talk in the Classroom (LTIC) Guide was designed to provide teachers with the education and support required to feel confident delivering mental health-related material in the Grade 7/8 classroom. The overall goal of this preliminary evaluation was to explore the acceptability, feasibility, and utility of the Guide using a mixed methods approach. A matched, pre/post-test evaluation of the Guide was conducted during the 2017/2018 school year among a sample of educators in Ontario, Canada (n = 42). Quantitatively and qualitatively, results demonstrated that teachers felt more confident and expressed fewer worries associated with teaching mental health-related lessons after engaging with the Guide and were suggestive of acceptability and utility, with continued challenges associated with feasibility identified.

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)0.013
metaresearch head score (Gemma)0.021
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 score0.960
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.167
GPT teacher head0.492
Teacher spread0.325 · 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

Citations3
Published2022
Admission routes4
Has abstractyes

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