The Readability of Electronic Cigarette Health Information and Advice: A Quantitative Analysis of Web-Based Information
Bibliographic record
Abstract
BACKGROUND: The popularity and use of electronic cigarettes (e-cigarettes) has increased across all demographic groups in recent years. However, little is currently known about the readability of health information and advice aimed at the general public regarding the use of e-cigarettes. OBJECTIVE: The objective of our study was to examine the readability of publicly available health information as well as advice on e-cigarettes. We compared information and advice available from US government agencies, nongovernment organizations, English speaking government agencies outside the United States, and for-profit entities. METHODS: A systematic search for health information and advice on e-cigarettes was conducted using search engines. We manually verified search results and converted to plain text for analysis. We then assessed readability of the collected documents using 4 readability metrics followed by pairwise comparisons of groups with adjustment for multiple comparisons. RESULTS: A total of 54 documents were collected for this study. All 4 readability metrics indicate that all information and advice on e-cigarette use is written at a level higher than that recommended for the general public by National Institutes of Health (NIH) communication guidelines. However, health information and advice written by for-profit entities, many of which were promoting e-cigarettes, were significantly easier to read. CONCLUSIONS: A substantial proportion of potential and current e-cigarette users are likely to have difficulty in fully comprehending Web-based health information regarding e-cigarettes, potentially hindering effective health-seeking behaviors. To comply with NIH communication guidelines, government entities and nongovernment organizations would benefit from improving the readability of e-cigarettes information and advice.
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 | 0.016 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".