Development of a functional electrical stimulation cycling toolkit for spinal cord injury rehabilitation in acute care hospitals: A participatory action approach
Bibliographic record
Abstract
The purpose of our study was to develop a toolkit to facilitate the implementation of functional electrical stimulation (FES) cycling for persons with a newly acquired spinal cord injury (SCI) in the acute care inpatient hospital setting. The researchers and community members used participatory action as a research approach to co-create the toolkit. We held two focus groups to develop drafts, with a third meeting to provide feedback, and a fourth meeting to evaluate the toolkit and determine dissemination strategies. Toolkit development followed the Planning, Action, Reflection, Evaluation cycle. We used an iterative design informed by focus group and toolkit consultant (SC) feedback. In focus group discussions, we included FES cycling champions (JK, DW) who led acute care implementation. Focus group members, recruited through purposive sampling, had to 1) have an understanding about FES cycling in acute care for SCI and 2) represent one of these groups: individual living with SCI, social support, hospital manager, clinician, therapist, researcher, and/or acute care FES cycling champion. Twelve individuals took part in four focus groups to develop a toolkit designed to facilitate implementation of FES cycling in SCI acute care in Edmonton, Alberta. Group members included an individual with lived experience, three acute-care occupational or physical therapists, three acute-care hospital managers, and five researchers. Two physical therapists also identified as clinical FES cycling champions. Following an inductive content analysis, we identified four main themes: 1) Health care provider toolkit content and categories, 2) Health care provider toolkit end product, 3) Collaborations between groups and institutions and 4) Infrastructure. Interested parties who utilize FES cycling in acute care for SCI rehabilitation agree that toolkits should target the appropriate group, be acute care setting-specific, and provide information for a smooth transition in care.
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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 | 0.083 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| 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".