Predictors of Change in Osteoporosis Knowledge, Health Beliefs, and Self-Efficacy After an Education Intervention
Bibliographic record
Abstract
Abstract Education interventions that increase osteoporosis knowledge and address health beliefs and self-efficacy help older adults make informed decisions to prevent and manage the disease. The aim of this study was to determine if clinical risk factors for osteoporosis moderate the effect of a multifaceted education intervention on osteoporosis knowledge, health beliefs, and self-efficacy. Patients 50 years and older with no prior diagnosis of or treatment for osteoporosis were referred by their primary care provider for bone mineral density testing by DXA and randomized to an osteoporosis education intervention group (n = 102) or usual care group (n = 101). Demographic and health history questionnaires, and validated tools to assess osteoporosis knowledge, health beliefs and self-efficacy were completed at baseline and 6-month follow-up. Results of the linear mixed-effects model showed a significant interaction with younger age (p=.024) on self-efficacy among patients in the intervention group compared to the usual care group. Patients with higher BMI had greater perceived health motivation (p=.026) in the intervention group. Compared to the usual care group, patients in the intervention group with higher vitamin D intake had greater perceived exercise (p=.020) and calcium benefits (p=.012) and those with a family history of osteoporosis had greater perceived susceptibility to osteoporosis (p=.045). By understanding the key factors that predict change in knowledge, health beliefs and self-efficacy after an education intervention compared to usual care, we can better tailor interventions to enhance prevention and management of osteoporosis.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".