Craving in Patients with Schizophrenia and Cannabis Use Disorders
Bibliographic record
Abstract
OBJECTIVE: Cannabis use is widespread among patients with schizophrenia despite its negative impact on the course of the disease. Craving is a considerable predictor for relapse in people with substance use disorders. Our investigation aimed to gain insight into the intensity and dimensions of cravings in patients with schizophrenia and cannabis use disorders (CUDs), compared with otherwise healthy people with CUDs (control subjects). METHOD: We examined 51 patients with schizophrenia and CUDs and 51 control subjects by means of the Cannabis-Craving Screening questionnaire. RESULTS: We found greater overall intensity of craving and greater relief craving in patients with schizophrenia and CUDs. Reward craving was greater in the CUDs group. Relief craving was associated with symptoms of schizophrenia in patients with schizophrenia and CUDs. CONCLUSION: Our findings are in line with the view that aspects of self-medication or affect regulation may account (at least in part) for cannabis use in people with schizophrenia. A better understanding of the dimensions of craving may help to improve targeted therapeutic interventions that aim to reduce drug consumption in this difficult-to-treat patient group.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".