Clinician perspectives on the implementation of inpatient cycling-based exergames for children with cerebral palsy: A qualitative study
Bibliographic record
Abstract
OBJECTIVE: To understand the perspectives of key stakeholder clinicians in implementing inpatient cycling-based exergames for children with cerebral palsy (CP) into pediatric rehabilitation. METHODS: Sixteen clinicians (nurses, physiotherapists, recreational therapists) that participated in a study exploring the feasibility of implementing cycling-based exergames for inpatient children with CP were interviewed. Clinicians' responses were coded using the Theoretical Domains Framework (TDF). Beliefs were generated from each response and relevant domains were identified. RESULTS: Ten domains from the TDF were identified as relevant. Key enablers to clinician participation in future implementation included revision of clinician roles and responsibilities, the belief that children with CP will benefit from exergames, and the belief in potential applicability to other pediatric populations. Barriers included clinician limited time and exergame responsibilities not aligning with perceived clinician roles. CONCLUSIONS: This study identifies key barriers and enablers that future inpatient pediatric rehabilitation settings should consider when incorporating exergames into rehabilitation practice.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".