Exploring the Experiences and Perceptions of Pediatric Therapists who use Functional Electrical Stimulation in their Clinical Practice
Bibliographic record
Abstract
Aim: This study aimed to 1) explore the experiences and perceptions of pediatric physical therapists (PTs) and occupational therapists (OTs) who use FES in their practice, and 2) provide recommendations for overcoming common barriers to FES implementation.Methods: Eight Canadian PTs (n = 6) and OTs (n = 2), who use FES in their pediatric practice, participated in semi-structured interviews. Open-ended questions queried the motivation, goals, perceived benefits and challenges, and facilitators and barriers of FES use. Interviews were audio recorded and transcribed verbatim. Interpretive description was used to analyze the transcripts.Results: One overarching theme emerged: FES is a useful adjunct to therapy for certain pediatric clients. Four sub-themes were identified: Participants described 1) motivation for incorporating FES into clinical practice, which led to 2) experiences with the implementation of FES in clinical practice, including strategies for overcoming implementation barriers. These experiences influenced 3) how FES is used in practice (e.g. for which populations and therapeutic goals), and informed 4) recommendations for pediatric FES practice (e.g. more educational opportunities, research, and access for families).Conclusions: Pediatric PTs and OTs who use FES in clinical practice view FES as a valuable adjunct, especially for motor training to improve functional skills.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".