Do alcohol industry-funded organisations act to correct misinformation? A qualitative study of pregnancy and infant health content following independent analysis
Bibliographic record
Abstract
BACKGROUND: Access to reliable, accurate, and up-to-date health information is a crucial component of global population health. Like other health-harming industries, the alcohol industry is known to provide misinformation to the public, including on alcohol, pregnancy, and infant health. It is unknown whether industry information changes following independent public health analysis. METHODS: We extracted data using the homepage, menu, and search tool functions (where available) from seven industry-funded charity and nonprofit company websites (Aware, South Africa; Drinkaware, Ireland; Drinkaware, United Kingdom; Éduc'alcool, Canada; DrinkWise, Australia; Foundation for Advancing Alcohol Responsibility, United States; and International Alliance for Responsible Drinking) that have previously been found to misrepresent the evidence on alcohol, pregnancy, and infant health. We conducted a qualitative, thematic analysis using a published framework of 'dark nudges and sludge' misinformation techniques. RESULTS: Omission of information, functionality problems, and the positioning and sequencing of information in ways that framed or obfuscated its meaning were the most common forms of misinformation identified. These types of misinformation were often mixed with (limited) relevant information and were most often found in combination. We found pregnancy and infant health information for the consumer on five of the seven websites studied (Drinkaware, Ireland; Drinkaware, United Kingdom; DrinkWise; Éduc'alcool; and Aware). Information on pregnancy and fetal alcohol spectrum disorder was found on these five sites, although they did not all provide information on miscarriage, breastfeeding, or fertility. We could not find any pregnancy and infant health information directed to the consumer on the remaining sites (Foundation for Advancing Alcohol Responsibility and International Alliance for Responsible Drinking). Six of the seven websites had a search tool function; these often produced irrelevant information. CONCLUSIONS: Following independent public health analysis of their informational outputs, misinformation about pregnancy and infant health remains present on alcohol industry-funded websites. Warnings to the public to avoid alcohol industry-funded information sources should form an essential part of the global effort to tackle health misinformation.
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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 | 0.040 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".