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Record W4307807292 · doi:10.1177/02724316221137954

Predictors of Employment in Early Adolescence: Results of a Longitudinal Study

2022· article· en· W4307807292 on OpenAlexafffund
Luc Laberge, Julie Auclair, Marc-Antoine Busque, Alexandre Maltais, Élise Ledoux

Bibliographic record

VenueThe Journal of Early Adolescence · 2022
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversité du Québec à MontréalInstitut de recherche Robert-Sauvé en santé et en sécurité du travailCégep de JonquièreUniversité du Québec à Chicoutimi
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsMultinomial logistic regressionPsychological interventionLogistic regressionPsychologyLongitudinal studyPopulationGerontologyDevelopmental psychologyDemographyMedicineEnvironmental healthSociologyPsychiatry

Abstract

fetched live from OpenAlex

The aim was to explore predictors of employment during the school year in adolescents aged 13. We report on a population-based sample of children followed-up from 5 months to this day. Parents and children answered questions on family, school, health, and work. A multinomial logistic regression was used to identify the predictors of informal and formal work. Results show that female sex and leisure time physical activity (LTPA) at age 12 were associated with an increased probability of doing informal work at age 13. Also, results indicate that 13-year-olds doing formal work for an employer or in the family business were more likely to report higher LTPA, to have used alcohol, to exhibit more delinquent behaviors, and to report lower educational aspirations at age 12. Such information must be used to devise interventions aiming at reducing the risks that school year employment may entail for education and health of adolescents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.292
Teacher spread0.263 · 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 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

Citations3
Published2022
Admission routes2
Has abstractyes

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