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 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 | 0.025 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".