Priorities in lower limb prosthetic service delivery based on an international survey of prosthetists in low- and high-income countries
Bibliographic record
Abstract
BACKGROUND: Prosthetic services, including the provision of an appropriate prosthesis, are a crucial part of the rehabilitation process for individuals with lower limb amputations. However, globally there exist unique challenges in the delivery of prosthetic services that are limiting rehabilitation outcomes and consequently the well-being and socio-economic status of individuals with lower limb amputations. OBJECTIVES: The objective of this work was to explore the issues related to the provision of appropriate prosthetic technologies and to compare these across different economies of the world. STUDY DESIGN: Cross-sectional survey. METHODS: An online survey was developed and distributed to prosthetic practitioners providing services in countries around the world. An open-coding thematic content analysis procedure was applied to extract key themes from the data. RESULTS: The response codes defined three overall themes of lower limb prosthetic delivery, and several key differences between higher and lower income countries emerged. Namely, a higher emphasis on part/material availability, practitioner training and durability in lower income countries was found. High costs were an issue raised by practitioners in all countries. CONCLUSION: Practitioners around the world share many of the same concerns; however, some lower income countries face important and pressing issues that limit their ability to provide adequate prosthetic services. CLINICAL RELEVANCE: This work highlights the most crucial service and technology-related needs, as perceived by trained prosthetic practitioners, of populations requiring lower limb prosthetic treatment around the world. Additionally, the results may be used to prioritize prosthetic-related health-care initiatives led by other researchers, governments and organizations working to improve services internationally.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".