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Record W7083689330 · doi:10.1080/13573322.2025.2563687

Love letters to school-based mental health: promoting a critical socio-ecological approach

2025· article· en· W7083689330 on OpenAlexaff

Bibliographic record

VenueSport Education and Society · 2025
Typearticle
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsSt. Francis Xavier UniversityUniversity of Regina
FundersAustralian Government
KeywordsQualitative researchTeaching methodCriticismMental health

Abstract

fetched live from OpenAlex

Despite calls for critical perspectives in mental health education, school-based approaches continue to lean heavily toward healthism, often with an overreliance on bio-medical approaches. In response, this research promotes improving criticality in school-based mental health education by examining the goals of mental health literacy in view of structural, ecological and social determinants of health. Drawing inspiration from the work of Barillas Chón et al. (2024) we adopt their methodology of writing critical love letters to express both appreciation and concern for current and future directions of the discipline. Through an examination of our professional connections to health education curriculum, we grapple with gaps in school-based mental health education, leading us to propose three additional goals for mental health literacy. The first goal encourages the use of counternarratives to understand how structural ideologies such as neoliberalism, patriarchy and colonialism impact mental health. The second turns a critical lens toward the Anthropocene, promoting the use of epistemological perspectives such as Indigenous pedagogies and environmental attunement to query how ecological factors contribute to inequities in mental health. The third new goal calls for challenging students to use high valence language to interrogate how living conditions impact populations that experience oppression. These new goals for mental health literacy offer additional pathways for promoting a critical socio-ecological approach in school-based mental health education, emphasizing criticality.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.669
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.316
Teacher spread0.303 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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 routes1
Has abstractyes

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