<i>Messages About Calcium and Weight</i> In Canadian Women's Magazines
Bibliographic record
Abstract
Purpose: Osteoporosis affects 1.4 million Canadians. Maximizing bone mass by age 30 may reduce this risk. Because calcium intake and body weight are both associated with bone mass, and many Canadian women report that they obtain nutrition information from magazines, we compared the frequency of calcium and body weight messages in Chatelaine and Flare, Canadian magazines for mature versus younger women, respectively. Methods: Using keywords, we identified relevant advertisements and articles in all issues of Chatelaine and Flare for 2000 to 2001. Data were analyzed using paired t-tests and Wilcoxon signed-rank sum tests. Results: Chatelaine had more calcium and weight messages per 100 pages than did Flare (significant only for calcium, p ≤0.0001). Within Chatelaine, there were no significant differences between the frequency of calcium and weight messages; however, almost 90% of Flare's messages focused on weight (p ≤0.0001), with only eight messages in two years addressing calcium. Conclusions: Magazines with limited calcium and many weight messages inadvertently promote a lifestyle that may increase the risk for osteoporosis. The opportunity exists to provide improved calcium and osteoporosis coverage for women at the prime age for maximizing bone density. Awareness of information gaps may help dietitians strategize in promoting nutrition messages to women.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".