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Record W2727859053 · doi:10.1192/bjp.bp.117.201418

Depressive and anxious symptoms and the risk of secondary school non-completion

2017· article· en· W2727859053 on OpenAlexaff
Frédéric N. Brière, Sophie Pascal, Véronique Dupéré, Natalie Castellanos‐Ryan, Francis Allard, Gabrielle Yale‐Soulière, Michel Janosz

Bibliographic record

VenueThe British Journal of Psychiatry · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAnxietyAssociation (psychology)ConfoundingPsychologyDepressive symptomsClinical psychologyPsychological interventionLogistic regressionModerationAcademic achievementMedicinePsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

Background Evidence regarding the association between adolescent internalising symptoms and school non-completion has been limited and inconclusive. Aims To examine whether depressive and anxious symptoms at secondary school entry predict school non-completion beyond confounders and whether associations differ by baseline academic functioning. Method We used logistic regression to examine associations between depressive and anxious symptoms in grade 7 (age 12–14) and school non-completion (age 18–20) in 4962 adolescents. Results Depressive symptoms did not predict school non-completion after adjustment, but moderation analyses revealed an association in students with elevated academic functioning. A curvilinear association was found for anxiety: both low and high anxious symptoms predicted school non-completion, although only low anxiety remained predictive after adjustment. Conclusions Associations between internalising symptoms and school non-completion are modest. Common school-based interventions targeting internalising symptoms are unlikely to have a major impact on school non-completion, but may prevent non-completion in selected students.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.005
GPT teacher head0.259
Teacher spread0.253 · 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 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

Citations17
Published2017
Admission routes1
Has abstractyes

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