Effect of multidisciplinary interventions using the Enabling Inclusion® program on gross motor function and functional independence in children with disabilities in rural South India
Bibliographic record
Abstract
BACKGROUND: The Enabling Inclusion® (EI®) program provides multidisciplinary, family-centred rehabilitation services for children with disabilities in rural South India. The program is supported by the Enabling Inclusion® app, and services are delivered collaboratively by community rehabilitation workers (CRWs) and rehabilitation specialists in children's homes or local early intervention centres. This study aimed to determine the effect of the EI® program interventions on gross motor function and functional independence in children with disabilities aged 0-9 years. METHODS: This retrospective cohort study analysed Gross Motor Function Measure (GMFM-88) and Functional Independence Measure for Children (WeeFIM®) scores collected at child enrolment in the EI® program and follow-up assessments conducted over 6 years. General linear model analyses were performed. RESULTS: The GMFM-88 and WeeFIM® follow-up data of 1245 participants (508 females and 737 males; mean age: 4.04 ± 2.3 years) were studied. The results demonstrated significant mean differences in both the GMFM-88 (p < 0.001, effect size - 0.356) and WeeFIM® (p < 0.001, effect size - 0.253) scores at all three time points. Female gender, centre-based intervention, and school enrolment were factors showing higher GMFM-88 and WeeFIM® scores. CONCLUSION: The EI® program's multidisciplinary interventions significantly improved gross motor function and functional independence of children with disabilities aged 0-9 years living in rural South India. This delivery model extends the reach of specialized rehabilitation in resource-limited areas through the use of technology and CRW services. These findings provide credence to the scaling of technology-assisted community-based rehabilitation models in underserved LMIC settings. TRIAL REGISTRATION: Clinical trial registration not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".