The elusiveness of representativeness in general population surveys for alcohol
Bibliographic record
Abstract
Population survey research is limited by biases introduced through the exclusion of sub-populations from the sampling frame and by non-response bias. This is a particular problem for alcohol surveys, where populations such as the homeless and the institutionalised-who consume on average more alcohol than the general population-are usually excluded, and where people who respond to alcohol surveys tend to consume less alcohol than those who do not. These biases lead to the underestimation of alcohol consumption at the population level, which can be corrected for by triangulating alcohol consumption data with population data sources (i.e. taxation and production). Other methods which account for the biases inherent in surveys include triangulation with outcomes (e.g. traffic injuries), calculation of estimates for groups which are outside common sampling frames, and combining probabilistic sampling with new methodologies, such as computer-assisted web interviews. In particular, population surveys do not attract sufficient participation numbers for certain groups, such as the marginalised urban male youths. In this situation, it may be helpful to add estimates generated via respondent-driven sampling or non-probabilistic web panels restricted to a specific group to such population surveys. Additionally, computer-assisted web interviews perform better for sensitive questions, such as those about personal alcohol use. In sum, based on the objectives, the future of survey will need to include statistical modelling, adding data from external sources for validation and combining data from various types of surveys.
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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 | 0.423 | 0.673 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".