Healthy midlife Canadian women: how bone health is considered in their food choice systems
Bibliographic record
Abstract
BACKGROUND: The incidence of osteoporosis is predicted to increase as Western populations age. Diet is considered to be an important modifiable factor in bone health, yet the diets of many women are insufficient in calcium and vitamin D, which comprise two key nutrients for bone health. This focus group study explored ways in which midlife women consider bone health in their personal food choice systems. METHODS: Data were obtained in six audio-recorded focus groups from a total of 36 women from upper, middle and lower income neighbourhoods. Open and axial coding and thematic analysis revealed shared and unique themes across and within the income groups. Use of member checks, peer debriefing, and independent and team data analysis enhanced rigour in the findings. RESULTS: All participants were aware of osteoporosis. Most women idealised making simple food decisions and eating for 'holistic' health, but not specifically for bone health. Most midlife women were not motivated to change their diets, few had deliberately increased their intake of calcium and vitamin D through foods and supplements, and few others had simplified their food decisions. CONCLUSIONS: Midlife women in the present study did not make eating for bone health a priority in their food choice systems. Instead, women wanted to eat for 'holistic' health, and only by implication bone health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".