Family Physicians' Personal and Practice Characteristics that Are Associated with Improved Utilization of Bone Mineral Density Testing and Osteoporosis Medication Prescribing
Bibliographic record
Abstract
Family physicians' personal and practice characteristics may influence how osteoporosis is managed. Thus, we evaluated the impact of family physicians' personal and practice characteristics on the appropriate use of bone mineral density testing and osteoporosis therapy. The physician questionnaire assessed 13 personal and practice characteristics of the physicians. The patient questionnaire was used to collect data to ascertain how family physicians managed osteoporosis. A total of 225 family physicians from 7 provinces across Canada completed both the physician and patient questionnaires. The family physicians evaluated a total of 5601 patients. The generalized estimating equations technique was utilized to model the associations between family physicians' personal and practice characteristics and appropriate use of bone mineral density testing and osteoporosis therapy. Odds ratios (OR) and corresponding 95% confidence intervals (CI) are reported. Findings indicated that female family physicians have higher odds of administering appropriate bone density testing compared to male family physicians (OR: 1.28; 95% CI: 1.05, 1.55), and that physicians who have hospital privileges (OR: 0.77; 95% CI: 0.62, 0.97) and who graduated more recently from medical school (OR: 0.87; 95% CI: 0.77, 0.99) have lower odds of administering appropriate bone mineral density tests. Physicians who use electronic health records have higher odds of administering appropriate therapy (OR: 1.30; 95% CI: 1.06, 1.59) as compared to physicians who do not use them. Several family physicians' personal and practice characteristics are associated with appropriate utilization of bone mineral density testing and therapy. The education of both clinicians and policy makers regarding these new insights may translate to enhanced individual practices and an improved overall health care system to optimize the environment for managing 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".