Gross Motor Family Report: Refinement and evaluation of psychometric properties
Bibliographic record
Abstract
AIM: To refine the Gross Motor Family Report (GM-FR) using parents' input and to evaluate its psychometric properties. METHOD: In this measurement study, 12 parents of children and adolescents with cerebral palsy (CP), aged 2 to 18 years, classified in all levels of the Gross Motor Function Classification System (GMFCS), were interviewed about their experience completing the GM-FR (content validity). Parents' feedback was used to refine the measure which was then completed by 146 families to evaluate internal consistency, and discriminative and concurrent validity. Forty-six parents completed the GM-FR again, 7 to 30 days later, to evaluate test-retest reliability. RESULTS: GM-FR scoring, pictures, descriptions, and the total number of items were revised based on parents' feedback. The GM-FR version 2.0 demonstrated high internal consistency (Cronbach's α = 0.99), no floor/ceiling effects, and excellent test-retest reliability (intraclass correlation coefficient = 0.99). GM-FR scores discriminated between GMFCS levels (p < 0.05) and were strongly negatively correlated with GMFCS level (r = -0.92; p < 0.001). GM-FR scores correlated positively and strongly with the Gross Motor Function Measure-66 (r = 0.94; p < 0.001) and the Pediatric Evaluation of Disability Inventory - Computer Adaptive Test mobility domain (r = 0.93; p < 0.001). INTERPRETATION: Active participation of families in the GM-FR's development facilitated creation of a family-friendly instrument. This study provides strong evidence of reliability and validity to support GM-FR use in clinical practice and research for assessing gross motor performance of children and adolescents with CP.
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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".