Use of telehealth for paediatric rehabilitation needs of Indigenous children – a scoping review
Bibliographic record
Abstract
Telerehabilitation is proposed as a promising avenue to enhance service accessibility for Indigenous communities, yet its application for Indigenous children remains relatively unexplored. This scoping review followed the PRISMA-ScR framework to explore current knowledge on the use of telerehabilitation for Indigenous children. Ten scholarly databases, seven grey literature databases, reference searches, and expert consultations were utilised to identify relevant studies. Included articles discussed the use of telerehabilitation provided by rehabilitation professionals (e.g. occupational therapist (OT), physical therapist (PT), speech and language pathologist (SLP) to Indigenous children and/or caregivers. Seven studies were included. Telerehabilitation was explored in different ways, the most common being real-time videoconferencing by SLPs. While some studies explicitly acknowledged cultural responsiveness within both the research process and the intervention, most were not designed for Indigenous children and their caregivers; rather, these participants were included with non-Indigenous participants. Successful implementation and sustainability of telerehabilitation services requires addressing technological limitations, understanding, and respecting diverse worldviews, and co-developing services to meet the unique needs of Indigenous families. Telerehabilitation has been rarely used with Indigenous children and when it was, little attention was given to cultural considerations. These findings emphasise that future telerehabilitation interventions should be truly community-led to ensure cultural relevance.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".