Love letters to school-based mental health: promoting a critical socio-ecological approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.101 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".