Problematic drinking and health problems: an epidemiologic study through general practitioners in the Tuscany Region (Central Italy) and Friuli Venezia Giulia Region (Northern Italy).
Bibliographic record
Abstract
"OBJECTIVES: to identify the differences among patients of general practictioners (GPs) in both Tuscany Region (Central Italy) and Friuli Venezia Giulia (FVG) Region (Northern Italy), which are different for drinking cultures, as to motivation of consultation, hazardous drinking and alcohol dependence, health problems, and use of health services. DESIGN: cross-sectional study by means of both a medical examination and a subsequent structured interview carried out with a questionnaire. Data were analysed using chi-square test, logistic regression and differences in prevalence. SETTING AND PARTICIPANTS: the study was implemented between July and November 2013 on a sample of 492 patients of 30 GPs in FVG, and 451 patients of 25 GPs in Tuscany. RESULTS: although patients in FVG were less likely to drink alcohol (66.7% vs. 70.9%), consumed lower amounts of alcohol on average per day per drinker (10.9 vs. 14.5 grams of alcohol), and were less likely to be hazardous drinkers (11.2% vs. 13.8%) compared to patients in Tuscany, they had a 3.6 to 4.7 times higher risk of alcohol dependence. In addition, the prevalence of diseases (in particular hepato-gastrointestinal diseases, hypertension, and psychiatric problems), smoking, and obesity/ overweighting was higher among clients of FVG, which exceed the Tuscan patients by 5-12 percentage points. Compared to Tuscany, FVG patients were more hospitalized and required more help to GPs or other people for their drinking problems. CONCLUSIONS: compared to Tuscan patients, GPs' patients in FVG has higher prevalence of alcohol addiction and other diseases, as well as of smoking and overweight/obesity, and higher need for health interventions as to their drinking problems."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".