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Record W7011443026

A Meta-Analytic Review of Gender Differences on Delay of Gratification and Temporal Discounting Tasks in ADHD and Typically Developing Populations

2018· other· en· W7011443026 on OpenAlexaff

Bibliographic record

VenueYorkSpace (York University) · 2018
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsYork University
Fundersnot available
KeywordsDelay of gratificationTypically developingGratificationDelay discountingTemporal discountingImpulsivityCognition
DOInot available

Abstract

fetched live from OpenAlex

Individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) tend to prefer smaller immediate rewards over larger delayed rewards compared to Typically Developing (TD) individuals. Currently it is unknown if males and females with ADHD differ in their preferences for delayed rewards, although females and males with ADHD appear to manifest differences in symptoms as well as in other cognitive and emotional domains. We used meta-analytic methods to examine gender differences on delay of gratification and temporal discounting tasks in both TD and ADHD samples. There were no differences between TD males and TD females, but males with ADHD were more likely to choose the larger delayed rewards than females with ADHD. These findings indicate a dissimilar pattern of gender differences for those with ADHD compared with TD samples. Implications of our findings are also discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.016
Bibliometrics0.0050.006
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.068
GPT teacher head0.324
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations0
Published2018
Admission routes1
Has abstractyes

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