Internet addiction, alcohol risky consumption, and gambling disorder among Palermo University Hospital: a cross-sectional study
Bibliographic record
Abstract
BacKGrOUnD: the aim of the study was to estimate the prevalence of gambling, internet addiction disease and the risk of alcohol consumption among workers of the “Paolo Giaccone” University hospital in Palermo. MethODs: the study employed a cross-sectional study design. an anonymous online survey was provided accompanied by informed consent. The questionnaire was structured into four parts. The first section investigates on socio-demographic information. in the other sections of the questionnaires were administered: internet addiction test, alcohol Use Disorders Identification Test-Consumption and Canadian Problem Gambling Index. A multivariable logistic regression model was used and adjusted odds ratios (aOr) are presented. RESULTS: The final sample size consists of 1482 subjects (response rate 79.42%). The 2.29% of the employees are at risk of pathological alcohol consumption, 0.47% has several problems due to the internet, and 2.77% are considered problematic gambling players. The several problems due to the internet is significantly associated with the following independent variables: age at increasing unit (aOr 0.71), at risk for consumption of alcohol (aOr 92.37), at risk for gambling (aOr 53.67). CONCLUSIONS: Along with the growing availability of information technology, individuals may experience adverse outcomes when internet usage is combined with alcohol risky consumption and gambling. since the increased access to addictive substances/behaviors leads to higher rates of addiction, future prospective studies and preventive measures should be implemented to reduce internet use, alcohol assumption and gambling in this population.
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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 | 0.000 |
| 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".