Understanding Preferences for Osteoporosis Information to Develop an Osteoporosis Patient Education Brochure
Bibliographic record
Abstract
CONTEXT: Patient education materials can provide important information related to osteoporosis prevention and treatment. However, available osteoporosis education materials fail to follow best-practice guidelines for patient education. OBJECTIVE: To develop an educational brochure on bone health for adults aged 50 years and older using mixed-method, semistructured interviews. DESIGN: This project consisted of 3 phases. In Phase 1, we developed written content that included information about osteoporosis. Additionally, we designed 2 graphic-rich brochures, Brochure A (photographs) and Brochure B (illustrations). In Phase 2, interviewers presented the text-only document and both brochure designs to 53 participants from an academic Medical Center in the Midwest and an outpatient clinic in the Southeastern region of the US. Interviewers used open- and closed-ended questions to elicit opinions regarding the brochures. In Phase 3, using feedback from Phase 2, we revised the brochure and presented it to 11 participants at a third site in the Southeastern US. MAIN OUTCOME MEASURES: Participants' comprehension of brochure text and acceptability of brochure design. RESULTS: We enrolled 64 participants. Most were women, white, and college-educated, with an average age of 66.1 years. Participants were able to restate the basic content of the brochure and preferred Brochure A's use of photographs. CONCLUSIONS: Using feedback from older adults, we developed and refined a brochure for communicating bone health information to older adults at risk of osteoporosis and fragility fractures. The methods outlined in this article may serve to guide others in developing health educational brochures for chronic medical conditions.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".