Teletherapy as an Alternate Model for Therapeutic Interventions for Children with Neurodevelopmental Disabilities in a Low-resource Setting in Jharkhand, India
Bibliographic record
Abstract
Abstract Background: This project was a collaboration between a community-based organization in rural India and a professional organization providing training and teletherapy support. The objective was to address the human resource gaps in rural areas for delivering therapeutic interventions to children with neurodevelopmental disabilities (NDD) in low-resource settings. Objective: To empower health workers in rural areas with the skills necessary to provide need-based therapeutic interventi ons to children with developmental disabilities through training and teletherapy. Methods: The project began with an initial training phase consisting of 10 online training sessions. This was followed by five additional need-based online training sessions over a 9-month period. Thirty-one children with NDD (15 girls and 16 boys) were enrolled and tracked using the Canadian Occupational Performance Measure (COPM). A total of 750 need-based therapy interventions, including physiotherapy, occupational therapy, and speech therapy, were provided by professional therapists via teletherapy. Results: The children showed significant improvement in the functional goals set from baseline to midline and end line. A one-way repeated-measures ANOVA test revealed a significant positive impact of teletherapy on the functional goals planned for the sample ( P < 0.5). Conclusion: Teletherapy demonstrated a positive impact on achieving functional goals for children with NDD in rural Jharkhand. It is recommended as an effective model to address human resource gaps and provide family-centered therapeutic support in low-resource rural areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".