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Record W4412595122 · doi:10.1093/heapro/daaf121

Mental health literacy in Nova Scotia’s education system: 15 years after the evergreen framework

2025· article· en· W4412595122 on OpenAlexaffabout
D. Patricia Page, Chris Gilham, Chloe Vukosa

Bibliographic record

VenueHealth Promotion International · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsSt. Francis Xavier UniversityUniversity of Toronto
Fundersnot available
KeywordsNova scotiaEvergreenMental healthNova (rocket)Health literacyLiteracyGeographyMedicineEnvironmental healthPsychologyGerontologyPolitical sciencePsychiatryPedagogyArchaeologyEngineeringEcologyHealth care

Abstract

fetched live from OpenAlex

This study examined the current state of mental health literacy (MHL)-related educator training, certification, and policy in Nova Scotia, Canada. It addressed three research questions: (i) How has Nova Scotia's educational policy landscape responded to the Evergreen Framework's (EF's) strategic directions? (ii) What are the strengths and gaps in current MHL-related policies? (iii) What sources of MHL training are available to preservice educators through Bachelor of Education (B.Ed.) and early childhood education (ECE) programs and to in-service educators through Master of Education (M.Ed.) programs and professional development (PD). A deductive content analysis was used to evaluate the alignment of provincial policies with the EF. An environmental scan and systematic analysis were conducted across ECE, B.Ed., and M.Ed. programs, as well as PD offerings and curriculum content. Findings indicate that while aspects of MHL are embedded within inclusive education policies and select course offerings, explicit training in MHL is limited and inconsistently implemented. PD opportunities and regional education center resources vary widely, and MHL competencies are not mandated for certification. The study provides recommendations for policy and training reform.

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)0.005
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.196
GPT teacher head0.635
Teacher spread0.438 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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