Assessing personal financial management in patients with bipolar disorder and its relation to impulsivity and response inhibition
Bibliographic record
Abstract
INTRODUCTION: Impulsivity and risk-taking behaviours are reported in bipolar disorder (BD). We examined whether financial management skills are related to impulsivity in patients with BD. METHODS: We assessed financial management skills using the Executive Personal Finance Scale (EPFS), impulsivity using the Barratt Impulsiveness Scale (BIS) and response inhibition using an emotional go/no-go task in bipolar individuals (N = 21) and healthy controls (HC; N = 23). RESULTS: Patients had fewer financial management skills and higher levels of impulsivity than HC. In patients and controls, increased impulsivity was associated with poorer personal financial management. Patients and HC performed equally on the emotional go/no-go task. Higher BIS scores were associated with faster reaction times in HC. In patients, however, higher BIS scores were associated with slower reaction times, possibly indicating compensatory cognitive strategies to counter increased impulsivity. CONCLUSIONS: Patients with BD may have reduced abilities to manage personal finances, when compared against healthy participants. Difficulty with personal finance management may arise in part as a result of increased levels of impulsivity. Patients may learn to compensate for increased impulsivity by modulating response times in our experimental situations although whether such compensatory strategies generalize to real-world situations is unknown.
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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 | 0.002 |
| 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 | 0.000 |
| 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".