Validation of the Adult Substance Abuse Subtle Screening Inventory-4 (SASSI-4)
Bibliographic record
Abstract
Abstract. The study objective was to develop a revision of the adult Substance Abuse Subtle Screening Inventory-3 to include new items to identify nonmedical use of prescription medications, as well as additional subtle and symptom-related identifiers of substance use disorders (SUDs) and to evaluate its psychometric properties and screening accuracy against a criterion of DSM-5 diagnoses for SUD. Clinical professionals throughout the nine US Census Bureau regions and two Canadian provinces who used the SASSI Online screening tool submitted 1,284 completed administrations of the provisional SASSI-4 along with their independent DSM-5 diagnoses of SUD. Validation sample findings demonstrated SASSI-4 sensitivity of 93% and specificity of 90%, AUC = .91. Items added to identify respondents who were abusing prescription medications showed 94% overall screening accuracy. Logistic regression showed no significant effects of client demographic characteristics or type of screening setting on the accuracy of SASSI-4 screening outcomes. In Study 2, 120 adults in recovery from SUD completed the SASSI-4 under instructions to fake good. Sensitivity of 79% was demonstrated for the full scoring protocol and was 47% when only face valid scales were utilized. Clinical utility is 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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".