Link Between Perception of Treatment Need and Craving Reports in Addiction
Bibliographic record
Abstract
Background Perception of treatment need (PTN), a component of clinical insight, is associated to negative addiction treatment outcomes when low. Our hypothesis was that lower PTN was associated with less craving when reported retrospectively, the most common measure of craving in clinical settings. Objective To explore the association between PTN and craving among a dataset of subjects with severe substance use disorders. Methods Participants were recruited from outpatient addiction clinic admissions or harm reduction program services. Good and low PTN were based on consistency between severe addiction (at least six DSM-5 criteria) and self-report need for addiction treatment from the Addiction Severity Index. Craving was retrospectively characterized over the past 30 days. Multiple regression analyses were conducted. Results Participants with low PTN (n = 97) retrospectively reported less frequent and intense episodes of craving, compared with participants with good PTN (n = 566) after controlling for sociodemographic factors, addiction type, and severity (p < 0.0001). Conclusion Low perception of treatment need among subjects with severe addictions is associated to less retrospective report of craving, which may contribute to reduced efficiency of treatment. Further studies are needed to explore the mechanisms of the association.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".