Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to estimate socioeconomic costs caused by alcohol drinking in Korea as of 2004 in an effort to raise the awareness of the gravity of problems associated with alcohol drinking and the necessity of active intervention by family physicians. METHODS: The costs were classified as direct costs, indirect costs and other costs. The direct costs consisted of direct medical costs and direct non-medical costs. The indirect costs were computed by the reduction and loss of productivity and the loss of workforce. Other costs consisted of property loss, administration costs and costs of alcohol beverage. RESULTS: The annual costs, which seemed to be attributable to alcohol drinking, were estimated to be 200,990 hundred million won (2.9% of GDP). In the case of the former, the amount included 38.83% for reduction of productivity, 26.92% for loss of the workforce, 22.24% for alcoholic beverage, 5.34% for direct medical costs, 2.29% for loss of productivity, 1.87% for direct non- medical costs, 1.54% for administration costs and 0.97% for loss of property. CONCLUSION: Our study confirms that compared with the cases of Japan (1.9% of GNP), Canada (1.09% of GDP), France (1.42% of GDP) and Scotland (1.19% of GDP), alcohol drinking incurs substantial socioeconomic costs to Koreans. An active intervention by family physicians is suggested.
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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 | 0.000 | 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) | 0.004 | 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".