Effect of executive functioning, decision-making and self-reported impulsivity on the treatment outcome of pathologic gambling
Bibliographic record
Abstract
BACKGROUND: Impairments in self-regulatory behaviour reflect a deficit in executive functioning and decision-making, as well as higher levels of self-reported impulsivity, and may be involved in the development and maintenance of addictive disorders. We sought to explore the association between self-reported impulsivity and neurocognitive measures, and their association with treatment outcome in pathologic gambling. METHODS: We assessed patients with pathologic gambling using executive functioning and decision-making tests and self-report measures of impulsivity. Patients underwent cognitive-behavioural therapy (CBT) for pathologic gambling. RESULTS: We included 88 patients (8% women) in our study. High self-reported extravagance was associated with poor performance in the Iowa Gambling Task (IGT)-ABCD version. High impulsiveness, low disorderliness, high exploratory excitability (trend), poor backward block span and poor IGT-EFGH scores (trend) predicted dropout. We observed no self-reported or neurocognitive predictors of relapse or number of treatment sessions attended. LIMITATIONS: Most participants were slot-machine gamblers seeking treatment. No follow-up data and no control group were included in the study. The missing sample (i.e., individuals who were recruited and assessed in the pretreatment stage but who chose not to begin treatment) had higher extravagance scores than the final sample. CONCLUSION: Neurocognitive reward sensitivity was related to self-reported overspending behaviour. Self-regulatory impairments (especially rash impulsiveness and punishment sensitivity) and executive dysfunction predicted only dropout of CBT in participants with pathologic gambling. Different neurocognitive processes and personality traits might mediate treatment response to psychological therapy of pathologic gambling according to the specific target variable assessed.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".