Cultural Knowledge in Context – People Aged 50 Years and Over Make Sense of a First Fracture and Osteoporosis
Bibliographic record
Abstract
Catch a Break (CaB) is a secondary fracture prevention program that uses medical understandings of osteoporosis to assess first fractures and determine appropriateness for secondary fracture prevention. In this study, we interviewed CaB program participants to identify the understandings that patients themselves used to make sense of first fractures and the osteoporosis suggestion as cause. Semi-structured interviews were conducted with female and male participants of the CaB program in Canada. An interpretive practice approach was used to analyze the data. A random sample of 20 individuals, 12 women, and eight men all aged 50 years and over participated. First fractures were produced as meaningful in the context of osteoporosis only for seniors of very advanced age, and for people of any age with poor nutrition. The trauma events that led to a first fracture were produced as meaningful only if perceived as accidents, and having an active lifestyle was produced as beneficial only for mental health and well-being unrelated to osteoporosis. Cultural knowledge shapes, but does not determine, how individuals make sense of their health and illness experiences. Risk prevention program designers should include patients on the design team and be more aware of the presumptive knowledge used to identify individuals at risk of disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".