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Record W4285043721 · doi:10.1080/17518423.2022.2099474

Clinician perspectives on the implementation of inpatient cycling-based exergames for children with cerebral palsy: A qualitative study

2022· article· en· W4285043721 on OpenAlexafffund
Analyssa Cardenas, Marina Petrevska, Elaine Biddiss, Darcy Fehlings

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalToronto Rehabilitation InstituteUniversity of Toronto
FundersCanadian Institutes of Health ResearchHolland Bloorview Kids Rehabilitation Hospital Foundation
KeywordsCerebral palsyPhysical medicine and rehabilitationCyclingPsychologyPhysical therapyAcquired brain injuryMedicineRehabilitation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.340
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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