Improving measurement of harms from others’ drinking: A key informant study on type and severity of harm
Bibliographic record
Abstract
AIMS: Some types of harms experienced because of others' drinking (AHTO) may produce greater negative effects than other harms. However, AHTO survey items were developed to measure type, not severity, of harm. We aimed to compare the perceived severity of a comprehensive list of AHTO items to assess consistency in subjective ratings of severity, facilitate a more nuanced analysis and identify strategies to improve measurement of AHTO in epidemiological surveys. METHODS: Thirty-six leaders of national alcohol surveys (conducted between 1997 and 2016) from 23 countries rated the typical severity of negative effects on the victim of each of 48 types of AHTO using a scale from zero (no negative effect) to 10 (very severe negative effect). The survey leaders were also asked to provide open-ended feedback about each harm and the severity-rating task generally. RESULTS: Of 48 harm items, five were classified as extreme severity (mean rating ≥8), 17 as high (≥6 <8), 25 as moderate (≥4 <6), and one as low (≤4). We used two-way random effects models to estimate absolute agreement intraclass correlation coefficients (AA-ICC) and consistency of agreement intraclass correlation coefficients (CA-ICC). Results showed that there was fair to excellent absolute agreement and consistency of agreement among "experts'' ratings of the severity of harms from others' drinking (single measures CA-ICC = 0.414, single measures AA-ICC = 0.325; average CA-ICC = 0.940, average AA-ICC = 0.914). Harms to children, and harms causing physical, financial, practical, or severe emotional impacts were rated most severe. CONCLUSIONS: When designing new AHTO surveys and conducting analyses of existing data, researchers should pay close attention to harms with high perceived severity to identify effective ways to prevent severe AHTO and reduce the negative health and social impacts of AHTO. In-depth analyses of specific sub-sets of harms and qualitative interviews with victims of severe AHTO may prove useful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".