Alcohol’s harm to others: An international collaborative project
Bibliographic record
Abstract
Callinan, S., Laslett, A., Rekve, D., Room, R., Waleewong, O., Benegal, V., Casswell, S., Florenzano, R., Hanh, H., Hanh, V., Hettige, S., Huckle, T., Ibanga, A., Obot, I., Rao, G., Siengsounthone, L., Rankin, G., & Thamarangsi, T. (2016). Alcohol’s harm to others: An international collaborative project. The International Journal Of Alcohol And Drug Research, 5(2), 25-32. doi:http://dx.doi.org/10.7895/ijadr.v5i2.218Aims: This paper outlines the methods of a collaborative population survey project measuring the range and magnitude of alcohol’s harm to others internationally.Setting: Seven countries participating in the World Health Organization (WHO) and ThaiHealth Promotion Foundation (ThaiHealth) research project titled “The Harm to Others from Drinking,” along with two other countries with similar studies, will form the core of a database which will incorporate data from other countries in the future.Measures: The WHO-ThaiHealth research project developed two comparable versions of a survey instrument, both measuring harm from others’ drinking to the respondent and the respondent’s children.Design: Surveys were administered via face-to-face methods in seven countries, while similar surveys were administered via computer-assisted telephone interviews in two additional countries. Responses from all surveys will be compiled in an international database for the purpose of international comparisons.Discussion: Harms from the alcohol consumption of others are intertwined with the cultural norms where consumption occurs. The development of this database will make it possible to look beyond reports and analyses at national levels, and illuminate the relationships between consumption, harms, and culture.Conclusions: This database will facilitate work describing the prevalence, patterning, and predictors of personal reports of harm from others’ drinking cross-nationally.
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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 | 0.083 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".