Alcohol use among medicine and law students in Poland
Bibliographic record
Abstract
Introduction. Alcohol, together with drug use such as marijuana, is a major health concern that may influence the life of both doctors and medicine students. It is therefore important to investigate their habits associated with those hazardous behaviors.Material and methods. A voluntary survey containing 12 questions regarding their drinking habits and marijuana use was sent to law and medicine students from two cities in Poland, Poznan and Lublin. 814 responses were collected and the results were compiled using STATISTICA 10 program.Results. Mean age of alcohol initiation was revealed to be very similar in all groups at below 16 years of age. Although majority of students drink less than once a week (41% male and 65.7% female), men were found to use alcohol much more frequently and in higher quantities than female students. Half of future doctors would stop at the lowest stage on a proposed alcohol intoxication scale, while 11.6% would venture to the highest, third one. Those values for law students were 36.2% and 26%, respectively. 70% of men and 52.9% of women have tried marijuana. Majority of them smoke less than once a month, but almost a quarter of law students and 15% of medicine students do it at least once a month.Conclusions. More emphasis should be put on educating future doctors and general public about dangers associated with hazardous drinking and cannabis use. Prevention of such behaviors should be conducted at an age as young as possible.
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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 | 0.001 |
| 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 | 0.000 | 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".