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Record W2148564962 · doi:10.1177/0956797611429708

Undercontrolled Temperament at Age 3 Predicts Disordered Gambling at Age 32

2012· article· en· W2148564962 on OpenAlexfundno aff
Wendy S. Slutske, Terrie E. Moffitt, Richie Poulton, Avshalom Caspi

Bibliographic record

VenuePsychological Science · 2012
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Drug AbuseNational Institute of Mental HealthNational Institute on AgingUniversité Laval
KeywordsTemperamentPsychologyDevelopmental psychologySocioeconomic statusCategorizationObservational studyCohortPersonalityClinical psychologyDemographySocial psychologyPopulation

Abstract

fetched live from OpenAlex

Using data from the large, 30-year prospective Dunedin cohort study, we examined whether preexisting individual differences in childhood temperament predicted adulthood disordered gambling (a diagnosis covering the full continuum of gambling-related problems). A 90-min observational assessment at age 3 was used to categorize children into five temperament groups, including one primarily characterized by behavioral and emotional undercontrol. The children with undercontrolled temperament at 3 years of age were more than twice as likely to evidence disordered gambling at ages 21 and 32 than were children who were well-adjusted at age 3. These associations could not be explained by differences in childhood IQ or family socioeconomic status. Cleanly demonstrating the temporal relation between behavioral undercontrol and adult disordered gambling is an important step toward building more developmentally sensitive theories of disordered gambling and may put researchers in a better position to begin considering potential routes to disordered-gambling prevention through enhancing self-control and emotional regulation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.209
GPT teacher head0.458
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; both teacher heads agree on what is shown here.

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

Citations103
Published2012
Admission routes1
Has abstractyes

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