Stakeholders and Contextual Factors in the Implementation of Assistive Robotic Arms for Persons With Tetraplegia: Deductive Content Analysis of Focus Group Interviews
Bibliographic record
Abstract
BACKGROUND: Tetraplegia imposes significant challenges on affected individuals, caregivers, and health care systems. Assistive technologies (ATs) such as assistive robotic arms have been shown to improve the quality of life of persons with tetraplegia, fostering independence in daily activities and reducing caregiver burden. Despite potential benefits, the integration of AT innovations into daily life remains difficult. Implementation science offers a systematic approach to bridge this know-do gap. OBJECTIVE: This study aimed to (1) identify and involve relevant stakeholders; (2) identify relevant contextual factors (barriers and facilitators); and (3) suggest a general outlook for the implementation of AT, specifically an assistive robotic arm, into the everyday private lives of individuals with tetraplegia in Switzerland. METHODS: A qualitative design was used, involving 3 semistructured online focus group interviews with 8 stakeholder groups, including persons with tetraplegia as well as those who could provide perspectives on engineering or technology, legal matters, nursing or care, therapy, social counseling, social insurance, and political considerations. The interviews were analyzed using the Focus Group Illustration Mapping tool, and the data were aligned with the domains of the Consolidated Framework for Implementation Research. RESULTS: 3 focus group interviews comprising 22 participants were conducted, and data were mapped onto 21 constructs across the Consolidated Framework for Implementation Research domains. Identified barriers were customization to users' needs, safety concerns, and financing issues for the high AT costs. The collaboration with different stakeholders, including those who provided perspectives on political engagement, proved crucial. Identified facilitators included the enhancement of autonomy for persons with tetraplegia, improvement of quality of life, reduction of caregiver dependency, and addressing health care labor shortages. The implementation outlook involved the formation of an experienced team and the development of an implementation plan using hybrid type 1 and type 2 designs that incorporate both qualitative and quantitative implementation and innovation outcomes. CONCLUSIONS: Robotic arms offer promising benefits in terms of improved participation for users, while high costs and regulatory complexities as to who will assume these costs limit their implementation. These findings highlight the complexities involved in implementing AT innovations and the importance of addressing contextual factors. A specific framework for the implementation of AT is needed to ensure the successful integration in Switzerland and other countries with comparable social and health insurance systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".