Calcium, Vitamin D, Iron, and Folate Messages in Three Canadian Magazines
Bibliographic record
Abstract
PURPOSE: Data from the Canadian Community Health Survey showed that calcium, vitamin D, iron, and folate are nutrients of concern for females 19-50 years of age. The study objectives were to assess the quantity, format, and accuracy of messages related to these nutrients in selected Canadian magazines and to examine their congruency with Canadian nutrition policies. METHODS: Using content analysis methodology, messages were coded using a stratified sample of a constructed year for Canadian Living, Chatelaine, and Homemakers magazines (n = 33) from 2003-2008. Pilot research was conducted to assess inter-coder agreement and to develop the study coding sheet and codebook. RESULTS: The messages identified (n = 595) averaged 18 messages per magazine issue. The most messages were found for calcium, followed by folate, iron, and vitamin D, and the messages were found primarily in articles (46%) and advertisements (37%). Overall, most messages were coded as accurate (82%) and congruent with Canadian nutrition policies (90%). CONCLUSIONS: This research demonstrated that the majority of messages in 3 Canadian magazines between 2003 and 2008 were accurate and reflected Canadian nutrition policies. Because Canadian women continue to receive much nutrition information via print media, this research provides important insights for dietitians into media messaging.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".