Examining the Factor Structure of the Substance Use Risk Profile Scale (SURPS) in Emerging Adults: An Exploratory Structural Equation Modelling Approach
Bibliographic record
Abstract
The Substance Use Risk Profile Scale (SURPS) measures personality traits linked to heavy drinking and related problems (hopelessness, anxiety sensitivity, impulsivity, sensation seeking) and informs personality-matching interventions. The SURPS’ factor structure shows inconsistencies, and evidence suggests that confirmatory factor analysis (CFA) is too restrictive for measures capturing correlated constructs. We examined if exploratory structural equation modelling (ESEM) better captured the optimal SURPS factor structure in a large Canadian sample, tested measurement invariance across sex and alcohol use differences, and assessed the predictive validity of SURPS subscales for alcohol use motives and problems. A sample of 6,397 emerging adults completed surveys. ESEM had excellent fit relative to CFA; Item 22 was removed due to a poor factor loading. The final model was invariant across groups; SURPS subscales predicted alcohol use motives and problems. Results support the SURPS’ utility for measuring substance use personality risk and ESEM’s utility for analyzing correlated constructs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".