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Record W2601799947 · doi:10.1111/cdev.12792

High School Dropout in Proximal Context: The Triggering Role of Stressful Life Events

2017· article· en· W2601799947 on OpenAlexafffundabout
Véronique Dupéré, Éric Dion, Tama Leventhal, Isabelle Archambault, Robert Crosnoe, Michel Janosz

Bibliographic record

VenueChild Development · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
FundersFonds de Recherche du Québec - SantéUniversité de MontréalSocial Sciences and Humanities Research Council of CanadaEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentFonds de Recherche du Québec-Société et CultureInstitut pour la Recherche en Santé Publique
KeywordsStressorPsychologyDrop outSchool dropoutDevelopmental psychologyPsychosocialContext (archaeology)Dropout (neural networks)Clinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Adolescents who drop out of high school experience enduring negative consequences across many domains. Yet, the circumstances triggering their departure are poorly understood. This study examined the precipitating role of recent psychosocial stressors by comparing three groups of Canadian high school students (52% boys; Mage = 16.3 years; N = 545): recent dropouts, matched at-risk students who remain in school, and average students. Results indicate that in comparison with the two other groups, dropouts were over three times more likely to have experienced recent acute stressors rated as severe by independent coders. These stressors occurred across a variety of domains. Considering the circumstances in which youth decide to drop out has implications for future research and for policy and practice.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.282
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations94
Published2017
Admission routes3
Has abstractyes

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