Alcohol Misuse in People with Schizophrenia: Findings from the Paddi (psychiatric and Addictive Dual Diagnosis in Italy) Study
Bibliographic record
Abstract
Background: Comorbid alcohol misuse in schizophrenia and severe mental illness is associated with significant clinical, social and legal problems. An epidemiologically informed approach to planning service delivery requires an understanding of which clinical populations are at particularly high risk for alcohol misuse. Most evidence about the prevalence of this comorbidity comes from the USA, Canada and Australia, and, though at a different pace, also from Europe. Method: A cross-sectional survey design has been used to determine the prevalence In Italy of comorbid drug and alcohol and any - even minor - mental disorders. Staff ratings was used to assess comorbid substance use. Results: Though overall dual diagnosis prevalence is around 2%, significantly higher rates were found in inner cities. Furthermore significant differences were found between different geographical areas (Northern vs. Centre vs. Southern Italy). Peculiar diagnostic subgroups showed higher risk to develop such comorbid condition, whilst a number of clinical and sociodemographic variables, including area of residence, were associated with the risk to develop a dependence syndrome. Conclusions: Sampling and assessment procedures are major limitations which might explain the lower rates as compared with the current Anglo-Saxon literature. However, such variability emphasizes also that high comorbid alcohol misuse rates are not necessarily the direct result of biological features inherent in schizophrenia, but that social factors play an important role. Finally, a number of risk factors associated with dual diagnosis could build up an evidence base about the nature of their substance use, providing targeted service planning and policy making.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".