MétaCan
Menu
Back to cohort
Record W2032935481 · doi:10.3109/17518423.2015.1004763

Exploring suitable participation tools for children who need or use power mobility: A modified Delphi survey

2015· article· en· W2032935481 on OpenAlexafffund
Debra A. Field, William C. Miller, Stephen E. Ryan, Tal Jarus, Lori Roxborough

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2015
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of TorontoToronto Rehabilitation InstituteUniversity of British ColumbiaHolland Bloorview Kids Rehabilitation HospitalGF Strong Rehabilitation CentreSunny Hill Health Centre for Children
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCanadian Occupational Therapy Foundation
KeywordsDelphi methodInclusion (mineral)RehabilitationGlobePsychologyMedical educationDelphiPopulationApplied psychologyGerontologyMedicineNursingComputer sciencePhysical therapySocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: To identify suitable tools for measuring important elements of participation for children, aged 18 months to 12 years, who need or use power mobility, and to indicate which tools should be considered for inclusion in a measurement toolkit. METHODS: Parents, therapists and researchers with expertise in paediatric power mobility and participation (n = 70) completed an online modified Delphi survey, with consensus set a priori >80% agreement. Existing tools were matched against participation elements ranked most important for those in early childhood (18 months-5 years) and of school-age (6-12 years) by the panel. RESULTS: Six out of 13 tools demonstrated potential, meeting at least three elements each, although none addressed all elements deemed important to measure by the panel. Only the Participation and Environment Measure for Children and Youth (PEM-CY) reached consensus for inclusion in a participation measure toolkit. CONCLUSION: Further evaluation of these tools with this population is warranted.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.309
GPT teacher head0.340
Teacher spread0.031 · 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

Citations8
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueDevelopmental NeurorehabilitationSame topicCerebral Palsy and Movement DisordersFrench-language works237,207