Examining the relation of personality factors to substance use disorder by explanatory item response modeling of DSM-5 symptoms
Bibliographic record
Abstract
This paper explores how personality factors affect substance use disorders (SUDs) using explanatory item response modeling (EIRM). A total of 606 Chinese illicit drug users participated in our study. After removing the cases with missing values on the covariate measures, a final sample of 573 participants was used for data analysis. The Diagnostic and Statistical Manual of Mental Disorders (DSM-5) was used to measure the illicit drug users' SUD level. Four personality factors-anxiety sensitivity, impulsivity, sensation seeking and hopelessness-along with gender and alcohol use were included in EIRM as person covariates. The results indicated that gender, alcohol use, and their interaction significantly predicted the SUD level. The only personality factor that strongly predicted the SUD level was sensation seeking. In addition, the interaction between gender and hopelessness was also found to be a significant predictor of the SUD level, indicating that the negative effect of hopelessness on SUD is stronger for women than for men. The findings suggest that sensation seeking plays an important role in influencing SUDs, and thus, it should be considered when designing intervention or screening procedures for potential illicit drug users. In addition, several DSM-5 SUD symptoms were found to exhibit differential effects by gender, alcohol use, and personality factors. The possible explanations were discussed.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".