The quality of nutritional information available on popular websites: a content analysis
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.
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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".