Telehealth for Indigenous Children Worldwide: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Indigenous children worldwide face healthcare disparities due, in part, to resource scarcity in remote settings which may be mitigated with technology. This study aims to determine the use of telehealth for this population, with respect to feasibility, acceptability, and the degree of patient/family involvement in reported interventions. We focused on the use of telehealth to support perioperative care. METHODS: To identify relevant studies, five databases were searched to find articles that focused on the role of telehealth in caring for Indigenous populations worldwide, with an emphasis on the pediatric population. Studies that lacked insight into those themes, as well as protocols and review articles, were excluded. Analysis was done according to the non-adoption, abandonment, scale-up, spread, and sustainability (NASSS) framework, the Montreal Model (patient involvement), and the theoretical framework of acceptability (TFA). RESULTS: Of the 1690 articles screened, 34 met the eligibility criteria. The most frequent uses of telehealth for Indigenous children were in ENT and psychiatry. Most of those had a low degree of complexity across the NASSS framework domains, suggesting greater feasibility. In 13 articles, the patient involvement was limited to information (lowest level of involvement in the Montreal Model). Only 11 articles directly assessed patient/family-perceived acceptability. Finally, two articles addressed telehealth in the surgical context. CONCLUSIONS: The relative simplicity of the proposed telehealth applications may support their sustained impact and use in other settings such as for perioperative care. Early and longitudinal involvement of communities is essential for responsible telehealth development that addresses local needs. LEVEL OF EVIDENCE: Level V.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".