A Comparative Content Analysis of Vegetarian Food Blogs Written by Registered Dietitians and Non-Registered Dietitians
Bibliographic record
Abstract
This purpose of this study was to compare the nutritional content of vegetarian recipes published in food blogs written by registered dietitians (RDs) and by non-registered dietitians (non-RDs). Twelve food blogs written by RDs and 12 written by non-RDs were selected using a systematic approach. For each food blog, 2 vegetarian entrée recipes per season were selected (n = 192 recipes). Descriptive analyses were performed using Fisher's exact test. Median nutritional values per serving between RDs' and non-RDs' recipes were compared using Wilcoxon-Mann-Whitney tests. RDs' recipes were significantly lower in energy, non-heme iron, vitamin C, and sodium, contained significantly more vitamin D and had a higher protein proportion than non-RDs' recipes. Disparities were also observed across type of entrée and vegetarian dietary pattern. In conclusion, this study showed that RD and non-RD food bloggers provided vegetarian recipes with few nutritional differences. Whether expanding the comparative analysis between RDs and non-RDs' blogs targeting different nutrition-related topics would yield different results remains to be investigated.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".