Screening questionnaires for substance abuse post brain injury: a review
Bibliographic record
Abstract
OBJECTIVE: To assess the psychometric properties of the available assessment questionnaires for substance abuse studied within a brain injury population. METHODS: A literature search was conducted on MEDLINE, PsycINFO, CINAHL, and Embase databases. Articles published in English from inception through March 2018 on the screening questionnaires used to identify substance abuse post brain injury were reviewed. Eligible primary studies had to include: adults (participants ≥18 years old) post brain injury; and report measures of diagnostic accuracy (e.g., sensitivity, specificity, and diagnostic odds ratio). RESULTS: Six screening questionnaires were included: Alcohol Use Disorders Identification Test, Brief Michigan Alcohol Screening Test, CAGE, Drug Abuse Screening Test, Substance Abuse Screening Inventory and the Short Michigan Alcohol Screening Test (SMAST). All questionnaires, except the SMAST, used the Diagnostic and Statistical Manual of Mental Disorders as the criterion measure. While report measures of diagnostic accuracy were reported and summarized, none of the studies provided reliability information or subgroup analysis among those with brain injury. CONCLUSIONS: Concerns of social desirability, population demographics, responsiveness to treatment effects, and administrative burden are important when selecting a questionnaire. Research examining the reliability of substance abuse screening questionnaires in the brain injury population is lacking and future research is warranted.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".