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Record W4417457478 · doi:10.1016/j.lana.2025.101310

The fine line between the cure and the illness: the risks of prescriptive emotionality and sociality for youth mental health

2025· article· en· W4417457478 on OpenAlexafffund
Aurélie Montagne, Cécile Rousseau, Ana Gómez-Carrillo

Bibliographic record

VenueThe Lancet Regional Health - Americas · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersFonds de Recherche du Québec - Santé
KeywordsSocialityMental healthPsychological resilienceConfusionUnintended consequencesResistance (ecology)Prosocial behaviorEmotionalityAdolescent development

Abstract

fetched live from OpenAlex

School-based initiatives are increasingly promoted as solutions to the youth mental health crisis, with Social Emotional Learning (SEL) among the most widely adopted frameworks worldwide. While designed to foster healthy socio-emotional development, evidence for SEL's long-term mental health benefits remains mixed. Concerns are also growing that universal, non-targeted SEL programs may inadvertently pathologize normal developmental experiences, reinforce self-monitoring, or generate cultural mismatches that undermine resilience. In this personal view, we examine key challenges associated with universal (i.e., non-targeted and intended for all students regardless of baseline risk) school-based programs modeled on SEL. While acknowledging their potential to promote youth well-being, we argue that prescriptive approaches to emotions and sociality can foster confusion among families, resistance among youth, and unintended distress. We highlight risks stemming from conceptual ambiguities and variability in implementation. Rather than abandoning universal programs, we call for rigorous evaluation, cultural adaptation, and integration within broader ecosocial-strategies to foster authentic, context-sensitive resilience in youth.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
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 head0.186
GPT teacher head0.441
Teacher spread0.255 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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