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Record W2079200519 · doi:10.1080/08035250500449866

Long-term relationships between symptoms of Attention Deficit Hyperactivity Disorder and self-esteem in a prospective longitudinal study of twins

2006· article· en· W2079200519 on OpenAlexaff
Tobias Edbom, Paul Lichtenstein, Mats Granlund, Jan‐Olov Larsson

Bibliographic record

VenueActa Paediatrica · 2006
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMedicineAttention deficit hyperactivity disorderLongitudinal studyProspective cohort studyCohort studyPopulationCohortAttention deficitPediatricsClinical psychologyPsychiatryDemographySurgery

Abstract

fetched live from OpenAlex

AIM: To study the long-term relationship between symptoms of Attention Deficit Hyperactivity Disorder and the developing self-esteem in a population-based sample of twins. METHODS: The cohort is all twin pair families born in Sweden between May 1985 and December 1986 (n = 1.480). Wave 1 took place in 1994 when the twins were 8 years old and wave 2 in 1999 when the children were 13 years old. In wave 1 and 2 the parents completed questionnaires regarding ADHD-symptoms about their children. In wave 2 the twins completed a questionnaire about self-esteem and Youth Self Report (YSR). ADHD-symptoms and self-esteem were analyzed in the total study group. RESULTS: There was a long-term relationship between high scores of parental-reported ADHD-symptoms at 8 and 13 years of age and low scores in measures of self-reported self-esteem at 13 years of age. In the cotwin control method controlling for YSR internalizing problem, paired comparisons within the twin pairs revealed that a high score of ADHD-symptoms at age 8 was related to significantly lower scores at age 13 in the self-esteem. CONCLUSIONS: The long-term relationships between ADHD-symptoms and a low self-esteem in a population-based sample were confirmed by the co-twin analyses.

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 score0.006
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.028
GPT teacher head0.294
Teacher spread0.265 · 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

Citations51
Published2006
Admission routes1
Has abstractyes

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