Improving measurement of harms from others' drinking: Using <scp>item‐response</scp> theory to scale harms from others' heavy drinking in 10 countries
Bibliographic record
Abstract
INTRODUCTION: The heavy drinking of others may negatively affect an individual on several dimensions of life. Until now, there is scarce research about how to judge the severity of various experiences of such harms. This study aims to empirically scale the severity of such harm items and to determine who is at most risk of these harms. METHODS: We used population-based survey data from 10 countries of the GENAHTO project (Gender and Alcohol's Harms to Others, data collection: 2011-2016). Questions about harms from others' drinking asked about verbal and physical harm, damage of belongings, traffic accidents, harassment, threatening behaviour, family and financial problems. We used item response theory methods (IRT) to scale severity of the aforementioned items. To acknowledge culturally based variations in different countries, we assessed 'differential item functioning'. RESULTS: The items 'family problems', 'financial problems' and 'clothes and property damage' as well as 'physical harm' were scaled as more severe in most countries compared to other items. Substantial differential item functioning was present in more than half of the country pairings. The item 'financial problems' was most often differentially scaled. Younger people who drank more, as well as women (compared to men), reported more harm. DISCUSSION AND CONCLUSIONS: Using IRT, we were able to evaluate grades of severity in harms from others' drinking. IRT scaling yielded in similar rankings of items as reported from other studies. However, empirical scaling allows for more differentiated severity scaling than simple summary scores and is more sensitive to cultural differences.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".