Important elements of measuring participation for children who need or use power mobility: a modified Delphi survey
Bibliographic record
Abstract
AIM: To identify and reach consensus on important elements of measuring participation in everyday life for children who need or use power mobility. METHOD: A panel (n=74) of parents, therapists, and researchers with pediatric power mobility and participation expertise completed an online modified Delphi survey. Three rounds determined important elements of participation for two groups: early childhood (18mo-5y) and school-aged (6-12y). 'Elements of participation' defined the 'who, what, where, and how' of measuring participation, generated from a literature review and participants' suggestions. Consensus was set a priori as ≥80% agreement. RESULTS: Consensus was reached on 21 out of 48 elements of participation important to measure for our population: eight elements for the younger group and 18 elements for the older group. When ranked by importance, four of the top five elements were common across both age groups. INTERPRETATION: For children using power mobility, measuring participation in a variety of settings is critical, along with considering both the child's and family's participation. Evaluating child engagement and enjoyment of participation are priorities, as is measuring barriers and facilitators. For school-aged children, evaluating child and parent reports of participation are essential. These elements can guide tool selection and/or development.
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 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 | 0.072 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".