Male and Non-English-Speaking Patients with Fracture Have Poorer Knowledge of Osteoporosis
Bibliographic record
Abstract
BACKGROUND: Prior fracture is a strong independent risk factor for subsequent fracture. To date, few studies have examined the level of osteoporosis knowledge specifically in the population of patients who have sustained a fracture. This study was designed to assess the knowledge of osteoporosis among patients who sustained a fracture and who were forty years of age or older, as well as to identify what social factors and health and fracture characteristics determine the level of osteoporosis knowledge in this population. METHODS: Patients who had sustained a fracture and were attending fracture clinics at two Toronto hospitals were identified and invited to fill out a questionnaire during their visit. This questionnaire included questions that could be answered by checking "true," "false," or "don't know" and that were designed to assess the patient's knowledge of osteoporosis. The questionnaire also included questions about the respondent's background. RESULTS: Of 259 patients identified as eligible for the study, 204 (78.8%) agreed to participate. The mean number of correct responses was 16.5 (55%) out of thirty responses. Variables significantly associated with greater numbers of correct responses were female sex, English as a first language, being currently employed, exercising regularly, and having received information from a health-care provider or from a newspaper or magazine. CONCLUSIONS: The level of osteoporosis knowledge was fairly low among the surveyed patients, indicating that more education is needed. This study also highlighted certain characteristics (i.e., male sex, English as a second language, being unemployed, and not exercising) that are associated with a lower level of knowledge. Our results can help target certain groups for osteoporosis educational initiatives, especially ethnic groups whose first language is not English, so as to appropriately reduce the risk of future fractures in this high-risk population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".