Understanding patient experiences and challenges to osteoporosis care delivered virtually by telemedicine: a mixed methods study
Bibliographic record
Abstract
This study sought to understand patient experiences, benefits, and challenges to osteoporosis care delivered virtually by telemedicine. Telemedicine bridges the access gap to specialized osteoporosis care in remote areas. Improving coordination of investigations, access to allied health members, and future initiatives may improve osteoporosis-related morbidity and mortality in this population. INTRODUCTION: There is limited research on the role of telemedicine (TM) in the management of osteoporosis (OP). We previously reported that OP patients assessed by TM had a higher prevalence of fragility fractures, co-morbidities, and need for allied health resources than those serviced by the outpatient clinic. The purpose of this study is to understand the experiences, benefits, and challenges associated with receiving OP care by TM from the patient perspective. METHODS: We adopted a convergent, mixed methods study design whereby both a quantitative component (mailed survey) and qualitative component (30-min telephone interviews) were conducted simultaneously. In addition to reporting survey data, thematic analysis was applied to interview data. RESULTS: Participants were comfortable with virtual technology and perceived that their quality of care by TM was comparable to in-person visits. Expressed benefits included the convenience of timely care close to home, reduced burden of travel and costs, and enhanced sense of confidence with being assessed by an osteoporosis specialist. Perceived barriers included poor follow-up with allied health professionals in the TM program (e.g., physiotherapist) and coordination of tests and investigations. Many participants indicated interest in an OP self-management program, with content focusing on diet and lifestyle factors. CONCLUSION: The TM program bridges the access gap for those living with OP in underserviced and remote areas. However, we identified the need to improve the existing processes to better coordinate access to allied health team members and arrangements for investigations. Participants also expressed interest for a virtual osteoporosis self-management program.
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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".