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Record W2419434709 · doi:10.1177/1087054716653214

The Influence of Socioeconomic Status on Psychological Distress in Canadian Adults With ADD/ADHD

2016· article· en· W2419434709 on OpenAlexaffabout
Emily Pond, Ken Fowler, Jacqueline Hesson

Bibliographic record

VenueJournal of Attention Disorders · 2016
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSocioeconomic statusPsychologyPsychological distressDistressClinical psychologyAttention deficit hyperactivity disorderDevelopmental psychologyPsychiatryMental healthEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The primary purpose of this study was to examine the relationship between socioeconomic status (SES) and psychological distress in individuals self-reporting a diagnosis of attention deficit disorder (ADD)/ADHD. METHOD: This correlational study encompasses cross-sectional data from 488 male and female adults (20-64 years) who reported that they have been diagnosed with ADD/ADHD. Psychological distress was measured with the Kessler Psychological Distress Scale (K10). RESULTS: Adults with ADD/ADHD and high incomes have significantly lower K10 scores than Canadians with ADD/ADHD and low incomes. Income, but not education, was significant in predicting psychological distress among the sample. Canadian adults with ADD/ADHD have an increased risk for developing psychological distress and comorbid psychiatric disorders. CONCLUSION: The findings suggest that negative outcomes associated with ADD/ADHD are not necessarily pervasive. High income may serve as a protective factor for psychological distress among adults with ADD/ADHD.

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 score0.075
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.303
Teacher spread0.290 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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