Setting a Cap on the Maximum Average Number of Drinks Per Day in Australian Survey Research
Bibliographic record
Abstract
Aims: The aim of this study is to assess the impact of the cap level on total consumption and a range of variables including negative consequences. Design, Setting, and Participants: Cross sectional survey in Australia with 2,020 Australians aged 16 and over.Measures: Participants completed a detailed survey on their alcohol consumption with no methodological limit on consumption.Findings: Setting a cap on high levels of consumption can significantly decrease the mean level of consumption. While the relationship between consumption and related variables do not change significantly, the relationship with negative consequences strengthens as the cap lowers, and the relationship with purchases decreases for uncapped data and data with low caps, providing some guidance on where a cap would be best placed.Conclusions: Consideration of where to set a maximum daily consumption level in survey research should be not only based on what could be feasibly consumed, but also on the point at which a very high reported consumption level is more likely to have been reported in error than as a reflection of reality. Checking the relationship between consumption and related variables, with different caps applied before selecting a capping level, is recommended.
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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 | 0.173 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".