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Record W2970544798 · doi:10.1515/ijamh-2017-0123

Blunted cortisol reactivity and risky driving in young offenders – a pilot study

2018· article· en· W2970544798 on OpenAlexafffund
Sophie Couture, Marie Claude Ouimet, Katarina Dedovic, Catherine Laurier, Pierrich Plusquellec, Thomas G. Brown

Bibliographic record

VenueInternational Journal of Adolescent Medicine and Health · 2018
Typearticle
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsInstitut universitaire en santé mentale de MontréalCollège de MaisonneuveInternational Centre for Comparative CriminologyUniversité de SherbrookeInstitut Universitaire en Santé Mentale de QuébecUniversité de MontréalMcGill UniversityDouglas Mental Health University InstituteDouglas College
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchFonds de recherche du Québec
KeywordsReactivity (psychology)PsychologyHuman factors and ergonomicsPoison controlMedicineMedical emergencyClinical psychology

Abstract

fetched live from OpenAlex

Adolescent risky driving is a significant burden on public health. Young offenders (i.e. under custody and supervision of the criminal justice system) may be particularly vulnerable, but research is scant. Previous work indicated that blunted cortisol reactivity to stress is a marker of risk-taking predisposition, including risky driving. In this study, we hypothesized that young offenders display higher levels of risky driving than a non-offender comparison group, and that cortisol reactivity contributes to the variance in risky driving independent of other associated characteristics (i.e. impulsivity, risk taking, alcohol and drug use). We found that young offenders (n = 20) showed riskier driving in simulation than comparison group (n = 9), and blunted cortisol reactivity was significantly associated with risky driving. The results suggest young offenders are prone to risky driving, and that individual differences in the cortisol stress response may be an explanatory factor.

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)0.000
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 score0.064
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.108
GPT teacher head0.399
Teacher spread0.291 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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