Patients’ Experiences of Nurse Case-Managed Osteoporosis Care: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Osteoporosis is a chronic condition that is often left untreated. Nurse case-managers can double rates of appropriate treatment in those with new fractures. However, little is known about patients' experiences of a nurse case-managed approach to osteoporosis care. OBJECTIVE: Our aim was to describe patients' experiences of nurse case-managed osteoporosis care. METHODS: A qualitative, descriptive design was used. We recruited patients enrolled in a randomized controlled trial of a nurse case-management approach. Individual semi-structured interviews were conducted which were transcribed and analyzed using content analysis. Data were managed with ATLAS.ti version 7. RESULTS: We interviewed 15 female case-managed patients. Most (60%) were 60-years or older, 27% had previous fracture, 80% had low bone mineral density tests, and 87% had good osteoporosis knowledge. Three major themes emerged from our analysis: acceptable information to inform decision-making; reasonable and accessible care provided; and appropriate information to meet patient needs. CONCLUSIONS: This study provides important insights about older female patients' experiences with nurse case-managed care for osteoporosis. Our findings suggest that this model to osteoporosis clinical care should be sustained and expanded in this setting, if proven effective. In addition, our findings point to the importance of applying patient-centered care across all dimensions of quality to better enhance the patients' experience of their health care.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".