Harmonising alcohol consumption, sales and related outcomes data across the UK and Ireland: an insurmountable barrier to policy evaluation?
Bibliographic record
Abstract
There is a need to ensure public health policies are robustly evaluated to establish their benefits and harms on the population and subgroups. We aimed to assess the comparability of Northern Ireland (NI) and Republic of Ireland (RoI) alcohol-related data to determine their suitability for evaluating the effectiveness of alcohol policies on alcohol consumption, sales, and related outcomes. A comparability analysis of NI and RoI alcohol-related hospital admissions, deaths, consumption, sales, and crime administrative and survey data was undertaken. Data sources were compared, where applicable, in terms of coding systems, population coverage, definitions, quality, response/completion rates, and question similarity. The NI and RoI consumption and sales data were found not to be comparable enough for use in a natural experiment study; comparability for hospital admission data was acceptable. Key barriers to comparability included variations in population coverage and lack of overlap in questionnaire topics. Data access issues made it difficult to fully determine data comparability for alcohol-related crime and deaths. By contrast, NI alcohol-related data were more comparable with other UK countries, making comparisons for the purpose of policy evaluation possible. RoI would benefit from identifying another economically and culturally similar country with comparable alcohol-related data.
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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 | 0.579 | 0.727 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".