A systematic review of the determinants of implementation of a locomotor training program using a powered exoskeleton for individuals with a spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Wearable powered exoskeletons represent a promising rehabilitation tool for locomotor training in various populations, including in individuals with a spinal cord injury. The lack of clear evidence on how to implement a locomotor powered exoskeleton training program raises many challenges for patients, clinicians and organizations. OBJECTIVE: To report determinants of implementation in clinical practice of an overground powered exoskeleton locomotor training program for persons with a spinal cord injury. DATA SOURCES: Medline, CINAHL, Web of Science. STUDY SELECTION: Studies were included if they documented determinants of implementation of an overground powered exoskeleton locomotor training program for individuals with spinal cord injury. DATA EXTRACTION: Eligible studies were identified by two independent reviewers. Data were extracted by one reviewer, based on constructs of the Consolidated Framework for Implementation Research, and validated by a second reviewer. RESULTS: Sixty-three articles were included. 49.4% of all determinants identified were related to the intervention characteristics, 29.6% to the individuals' characteristic and 13.5% to the inner setting. Recurrent barriers identified were the high prevalence of adverse events (e.g., skin issues, falls) and device malfunctions. Adequate training for clinicians, time and resource available, as well as discussion about patients' expectations were identified as facilitators. CONCLUSIONS: Powered exoskeleton training is a complex intervention. The limited information on the context and the implementation process domains may represent a barrier to a successful transition from knowledge to action.
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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 | 0.022 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".