The 2021 Alcohol’s Harm to Others Survey: Methodological Approach
Bibliographic record
Abstract
Abstract Background The 2021 Alcohol's Harm to Others (AHTO) is a comprehensive survey measuring the prevalence of different harms due to another’s drinking in the Australian population. First implemented in 2008, the AHTO survey has since been adapted to reflect changes in modern survey research and to be comparable with international AHTO surveys. Aims The current paper aims to provide a detailed account of the 2021 Australian Alcohol's Harm to Others (AHTO) survey, including the procedures for sampling, data collection, weighting, response rate calculation and results from a mode analysis. Methodology The 2021 AHTO survey was conducted by the Social Research Centre (SRC), whereby 1,000 participants were recruited through Random Digit Dial (RDD) and 1,574 through the Life in Australia Panel (LinA). Weights applied to the data to match key respondent demographics to the Australian population and between the two samples. Multivariable logistic regression models were conducted to probe the extent sample source (RDD; LinA) was associated with various survey outcomes. Results Multiple regression analyses found sample source had a statistically significant association with responses on three out of eight outcomes, with sample source contributing 1 – 8% of the overall variance in these models. Discussion The current paper highlighted the 2021 AHTO survey’s comprehensiveness and adaptability to a modern research context as its strengths. Yet some limitations were identified relating to the use of bi-modal survey methods. The methodological critiques from the current paper are vital to inform future AHTO surveys used in both a national and international context.
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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 | 0.136 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".