Non-pharmacological management of osteoporotic vertebral fractures: health-care professional perspectives and experiences
Bibliographic record
Abstract
PURPOSE: To understand experiences and perceptions on non-pharmacological treatment of vertebral fractures and virtual-care from the perspective of care professionals' (HCPs). DESIGN AND SETTING: We conducted semi-structured interviews with 13 HCPs within Canada (7 F, 6 M, aged 46 ± 12 years) and performed a thematic and content analysis from a post-positivism perspective. RESULTS: Two themes were identified: acuity matters when selecting appropriate interventions; and roadblocks to receiving non-pharmacological interventions. We found that treatment options were dependent on the acuity/stability of fracture and were individualized accordingly. Pain medication was perceived as important, but non-pharmacological strategies were also considered helpful in supporting recovery. Participants discussed barriers related to the timely identification of fracture, referral to physiotherapy, and lack of knowledge among HCPs on how to manage osteoporosis and vertebral fractures. HCPs reported positive use of virtual-care, but had concerns related to patient access, cost, and comprehensive assessments. CONCLUSION: HCPs used and perceived non-pharmacological interventions as helpful and selected specific treatments based on the recency of fracture and patient symptoms. HCPs' also believed that virtual-care that included an educational component, an assessment by a physiotherapist, and an exercise group was a feasible alternative, but concerns exist and may require further evaluation.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".