After the Fall: Women's Views of Fractures in Relation to Bone Health at Midlife
Bibliographic record
Abstract
Past research has established the link between low energy fractures and the risk for future fractures. These fractures are potential markers for investigation of bone health, and may be precursors for osteoporosis. In spite of its significant public health burden, including burden of illness and economic costs, many individuals are not aware of the risk factors for and consequences of osteoporosis. This is a study of women aged 40 and older who experienced low energy fractures (e.g., from non-trauma sources and falls from no higher than standing height). We gathered data, using focus group interviews, about their experiences and understanding of the fractures in relation to bone health. Women often attributed the fractures to particular situations and external events (e.g., slipping on ice, tripping on uneven ground), and viewed the fractures as accidents. Women often felt that others are at risk for poor bone health, but believed that they themselves are different from those really at risk. Although the fractures are potential triggers for preventive efforts, few women connected their fracture to future risk. What is perceived by women (and others) as random and an accident is often a predictable event if underlying risk factors are identified. Only when there is more awareness of poor bone health as a disease process and fractures as markers for bone fragility will women, men and health care providers take action to prevent future fractures and established bone disease.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".