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Record W323915348 · doi:10.1177/070674371405900904

Attention Deficits and Divorce

2014· article· en· W323915348 on OpenAlexafffundvenue
Geneviève Bouchard, Jean Saint‐Aubin

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2014
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCognitionInterpersonal communicationDevelopmental psychologyTask (project management)PopulationExecutive functionsInterpersonal relationshipPsychiatrySocial psychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Building on previous work on the role of attention deficits associated with the regulation of executive control in psychiatric disorders, we examine whether these attention deficits are related to an interpersonal disturbance, the experience of divorce. METHOD: Attentional capacities of 95 randomly selected couples from the general population were measured with a well-established task, the Attentional Network Task, which assesses the efficiency of 3 attention networks (that is, alerting, orienting, and executive control). Among the 190 participants, 32 had experienced a divorce in the past. ANCOVAs were used to compare divorced people in marital or cohabiting unions with people in first unions in their performance on this purely cognitive task. RESULTS: Our findings indicate that divorced people who are currently living in a cohabiting relationship show significantly lower executive control than other adults living as couples, after controlling for sex, age, income, and education. This subgroup of divorced people not only exhibit greater difficulty in responding to some stimuli while ignoring irrelevant ones but also manifest cognitive deficits in conflict resolution. CONCLUSIONS: This study highlights the links between attention and the long-term maintenance of intimate relationships. Our results may have important implications for the identification of people at risk for divorce.

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 score0.275
Threshold uncertainty score0.911

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 head0.013
GPT teacher head0.307
Teacher spread0.294 · 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

Citations21
Published2014
Admission routes3
Has abstractyes

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