Bibliographic record
Abstract
This article is an overview of different approaches to measuring alcohol consumption: self-reports and objective measures such as blood alcohol concentration (BAC) and aggregate level measures. These approaches are evaluated as regards their ability to capture quantity, frequency, volume and variability of drinking. This review focuses on self-report measures and on the current knowledge of undercoverage error when compared with sales data. In the comparative evaluation of measures, two analytical aims are examined: a) description and testing of differences across groups for which ordinal information is sufficient and b) establishment of cutoff points and risk relationships for which unbiased interval scale level is required. First, minimal differences were found between self-report measures when the recall period was sufficiently long enough. Second, prospective diaries appear to be stronger measures than retrospective recalls. However, prospective diaries commonly cover only short reporting periods and should be combined with simple retrospective measures to capture rare and infrequent drinking episodes. In regard to undercoverage, the discrepancy cannot be fully explained by non-response or concealment of consumption by drinkers. It is argued that undercoverage of sales data may be more related to sample frame defects–-e.g., the non-inclusion of particular subpopulations such as the homeless or institutionalized.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".