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Record W3018509217 · doi:10.1093/rof/rfaa013

ADHD Symptoms and Financial Distress

2020· article· en· W3018509217 on OpenAlexafffund
Chi Liao

Bibliographic record

VenueEuropean Finance Review · 2020
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDistressAttention deficit hyperactivity disorderFinancial distressPsychiatryPsychologyPaymentClinical psychologyFinanceEconomics

Abstract

fetched live from OpenAlex

Abstract We examine the effect of attention-deficit/hyperactivity disorder (ADHD) on individual-level financial distress. ADHD is the most common mental disorder among children and is characterized by behaviors such as inattention, hyperactivity, and impulsiveness that interfere with school and home life. In a representative panel, we find that individuals with more severe ADHD symptoms during childhood have more difficulty paying bills and are more likely to be delinquent on bill payments in adulthood. Further, those with more severe symptoms are less likely to have precautionary savings and more likely to have to delay buying necessities. These effects exist across the full range of ADHD symptom scores and are not driven by the most severe cases of ADHD; this is consistent with recent evidence that ADHD symptoms occur on a continuum. Preliminary evidence suggests that medication for behavioral issues may mitigate the effect of ADHD symptoms on financial distress.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.306
Teacher spread0.257 · 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 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

Citations34
Published2020
Admission routes2
Has abstractyes

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Same venueEuropean Finance ReviewSame topicAttention Deficit Hyperactivity DisorderFrench-language works237,207