Understanding the adoption of digital workflows in orthotic & prosthetic practice from practitioner perspectives: a qualitative descriptive study
Bibliographic record
Abstract
BACKGROUND: The implementation of digital technology (DT) in orthotics and prosthetics (O&P) has been slow despite recent research suggesting that the use of DT will continue to grow and become more prevalent within the industry. There is a need to further investigate DT in O&P practice and the current state of its use in the field. OBJECTIVE: This study aimed to explore the views and experiences of practitioners using DT workflows in their O&P practice. METHODS: In this qualitative descriptive study, 10 in-depth, semistructured interviews with O&P practitioners were conducted. A content analysis was performed to analyze the transcripts and identify key themes from the data. RESULTS: The study examined the experiences of practitioners using or trying to use DT in their practices, and three key themes were identified on the implementation of digital practice: 1) technological advancement and scientific evidence; 2) marketplace, economic, and operational factors; and 3) industry mindset shift in embracing DT practice. CONCLUSION: A collaborative effort involving academia, healthcare institutions, vendors, and individual practitioners will be required to facilitate the widespread adoption of DT in O&P. More work is required to overcome challenges from the technical, logistical, and cultural aspects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".