The quality of nutritional information available on popular websites: a content analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The overall purpose of this study was to increase knowledge and understanding of the new informational landscape that is emerging on the Internet in relation to nutritional health content in order to provide policy makers with better communication and health promotion tools. We identified the sites most used by Canadians to access nutrition information and conducted content analyses to identify the sources of this nutritional information as well as its quality by systematic comparison with the main guidelines published in the Canada Food Guide. We found that commercial websites accounted for 80% of visits and time spent on seeking health and nutrition information. We also found uneven messaging about fruit and vegetable intake as well as consistent messaging undermining the 'eat a variety of foods' message, which is a central component of the Canada Food Guide. On the positive side, inappropriate or incongruent advice about salt, coffee and alcohol intake was virtually non-existent and advice congruent with the guide was found three times more often than incongruent advice. Finally, the site offering the best advice was a non-commercial government-based site. This site differed from the commercial sites not so much in its ability to deliver the 'right' advice but more in its ability to exclude articles with poor and misleading advice on their sites.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it