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Record W3019919922 · doi:10.1177/1087054720915246

Pathways From ADHD Symptoms to Suicidal Ideation During College Years: A Longitudinal Study on the i-Share Cohort

2020· article· en· W3019919922 on OpenAlexaff
Julie Arsandaux, Massimiliano Orri, Marie Tournier, Antoine Gbessemehlan, Sylvana M. Côté, R. Salamon, Christophe Tzourio, Cédric Galéra

Bibliographic record

VenueJournal of Attention Disorders · 2020
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversité de MontréalMcGill University
FundersConseil Français de l'ÉnergieAgence Nationale de la Recherche
KeywordsSuicidal ideationMediationPsychologyDepressive symptomsClinical psychologyCohortPsychiatryProspective cohort studyCohort studyLongitudinal studyPoison controlInjury preventionMedicineInternal medicineCognitionMedical emergency

Abstract

fetched live from OpenAlex

Objective: To estimate the association between ADHD symptoms and suicidal ideation in college students, and to test mediation by depressive symptoms or self-esteem. Method: Based on the i-Share cohort (prospective cohort of 2,331 college students in France). Self-reported measures included ADHD symptoms at baseline, self-esteem and depressive symptoms at 3 months, and suicidal ideation at 1-year follow-up. We conducted path analysis to estimate total, direct, and indirect effect. Results: Participants with high ADHD symptoms were more likely to report suicidal ideation 1 year later ( p < .0001). Indirect effects through depressive symptoms ( p < .0001) and self-esteem ( p < .0001) explained 44% and 25% of this association, respectively. An indirect pathway via a combination of self-esteem, then depressive symptoms, was also identified ( p < .0001), explaining 19% of the total effect. The direct effect was not significant ( p = .524). Conclusion: ADHD symptoms seem to have no direct but indirect effect through both self-esteem and depressive symptoms on suicidal ideation.

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 score0.020
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.314
Teacher spread0.249 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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