Patients' adherence to osteoporosis therapy: exploring the perceptions of postmenopausal women.
Bibliographic record
Abstract
OBJECTIVE: To explore the experiences and perceptions of postmenopausal women regarding strategies to improve adherence to osteoporosis therapy. DESIGN: Qualitative, mixed phenomenologic study using focus groups. SETTING: Family physicians' and specialists' practices and community pharmacies in Hamilton, Ont. PARTICIPANTS: A total of 37 postmenopausal women currently taking at least 1 prescription or over-the-counter medication for osteoporosis. METHOD: Focus groups were conducted using a semistructured interview guide consisting of 10 open-ended questions about patients' perceptions of their osteoporosis medications, their reasons for adherence and non-adherence to therapy, and the effectiveness of strategies they had tried to improve adherence. At least 2 research team members analyzed the data to find primary themes. MAIN FINDINGS: Analysis of data from the 7 focus groups found 6 main factors that influenced adherence to medications: belief in the importance of taking medications for osteoporosis, medication-specific factors, beliefs regarding medications and health, relationships with health care providers, information exchange, and strategies to improve adherence. Strategies that facilitated adherence to medications included having a system for taking medications, using cues or reminders, being well informed about the reasons for taking medications, and having regular follow-up by health care providers for support and monitoring after having been prescribed medications. CONCLUSION: Results of this study provide a better understanding of how patients' perceptions and experiences affect their adherence to osteoporosis medications. Because each patient's reasons for non-adherence might be different, depending on individual beliefs or circumstances, strategies to improve adherence to medications should be individualized accordingly.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".