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Record W4385843442 · doi:10.15273/hpj.v3i2.11585

Measurement and Design in Surveys of Teachers’ Mental Health Literacy: A Scoping Review

2023· review· en· W4385843442 on OpenAlexaffabout
Chris Gilham, Taylor G. Hill, Emma C. Coughlan, Damian Page, Chloe Vukosa, Alyssa Spridgeon, Peter Wilkie, Kristie Przewieda

Bibliographic record

VenueHealthy Populations Journal · 2023
Typereview
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of TorontoDalhousie UniversitySt. Francis Xavier University
Fundersnot available
KeywordsMental health literacyConceptualizationVignetteMental healthPsychologyPsychological interventionLiteracyMedical educationApplied psychologyMedicinePedagogyMental illnessSocial psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Mental health literacy (MHL) was introduced four decades ago as a term referring to knowledge and beliefs about mental disorders that aid in their recognition, management, or prevention. This scoping review mapped the peer-reviewed literature to understand how MHL is defined, conceptualized, and measured in studies involving those becoming teachers (pre-service teachers) and working teachers (in-service teachers). The search was performed following the method for scoping reviews by the Joanna Briggs Institute (JBI). Searches were conducted in four scientific databases with no time limit, although all sources had to be written in English or French. Primary studies (N = 35) that measured MHL for pre- and in-service teachers provided a global snapshot of MHL conceptualization and measurement across five continents. Global conceptualizations of MHL were largely driven by the definition and measures developed by Jorm, though the definition by Kutcher et al. was used in one fourth of the papers. Few studies explicitly stated a theoretical framework. Most studies used closed-ended scales, or a combination of closed-ended scales and vignettes to measure MHL. From a closer examination of the results, Canada emerged as a major leader in teacher MHL. Future research in this area should aim to include vignette measures, especially for pre-service teachers, and explicit theoretical frameworks, including socio-ecological and social or structural determinants of health-related frameworks that take an intersectional approach to MHL.

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)0.044
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.695
GPT teacher head0.613
Teacher spread0.082 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2023
Admission routes2
Has abstractyes

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