Addictive Behaviors in Comorbid Addiction and Mental Illness: Preliminary Results from a Self-Report Questionnaire
Bibliographic record
Abstract
OBJECTIVE: : The objective of this study was to pilot a new self-report screening instrument, the addictive behaviors questionnaire (ABQ), which screens for a broad range of impulsive-compulsive behaviors, in a treatment-seeking psychiatric population with comorbid addiction. METHODS: : Psychiatric outpatients (N = 94) being treated for comorbid addiction and mental illness were approached consecutively by their therapists to fill out a number of self-report measures, in addition to the ABQ, including the Addiction Severity Index, Toronto Alexithymia Scale, and Reflective Activity Scale. The ABQ was evaluated both as a continuous measure and as a categorical measure using clinically relevant cutoff scores for each behavior. It was tested for internal reliability, test-retest reliability, and correlation with other scales. RESULTS: : The completion rate for the ABQ was 74%. It demonstrated good internal reliability (continuous measure α = 0.81, categorical measure alpha = 0.82) and good test-retest reliability (continuous r = 0.68, categorical r = 0.76). The total ABQ score positively correlated with alexithymia (continuous r = 0.45, P < 0.001; categorical r = 0.37, P < 0.001) and negatively correlated with reflective activity (continuous r = -0.29, P < 0.01; categorical r = -0.27, P < 0.05). The behaviors that were most commonly endorsed as problematic were overeating, unhealthy relationships, excessive TV watching, and excessive shopping. Based on cutoff scores, 61% of the sample endorsed at least one problematic behavior and 31% endorsed 2 or more behaviors. CONCLUSIONS: : These preliminary results suggest that the ABQ is reliable and easily administered by clinicians treating comorbid addiction in an outpatient setting. Further study is required with larger sample sizes, normative data, and comparable scales to help establish construct validity and cutoff scores.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".