How are family physicians managing osteoporosis? Qualitative study of their experiences and educational needs.
Bibliographic record
Abstract
OBJECTIVE: To explore family physicians' experiences and perceptions of osteoporosis and to identify their educational needs in this area. DESIGN: Qualitative study using focus groups. SETTING: Four Ontario sites: one each in Thunder Bay and Timmins, and two in Toronto, chosen to represent a range of practice sizes, populations, locations, and use of bone densitometry. PARTICIPANTS: Thirty-two FPs participated in four focus groups. Physicians were identified by investigators or local contacts to provide maximum variation sampling. METHOD: Focus groups using a semistructured interview guide were audiotaped and transcribed. The constant comparative method of data analysis was used to identify key words and concepts until saturation of themes was reached. MAIN FINDINGS: Family physicians order bone densitometry and try to manage osteoporosis appropriately, but lack a rationale for testing and are confused about management. Participants' main concern was clinical management, followed by disease prevention and their educational needs. CONCLUSION: Family physicians are confused about how to manage osteoporosis. To reduce the burden of illness due to osteoporosis, educational interventions should be tailored to family physicians' needs.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".