Culturally Safe eHealth Interventions With Aboriginal and Torres Strait Islander People: Protocol for a Best Practice Framework
Bibliographic record
Abstract
BACKGROUND: There is growing global evidence on the adoption and effectiveness of eHealth (including mobile health and telehealth) by First Nation peoples including Aboriginal and Torres Strait Islander people. Although there are frameworks to guide eHealth development, implementation, and evaluation, it is unknown whether they adequately encapsulate the health, cultural, and community-related priorities of Aboriginal and Torres Strait Islander people. OBJECTIVE: The aim of this research program is to prepare a best practice framework that will guide the co-design, implementation, and evaluation of culturally safe eHealth interventions within existing models of health care for Aboriginal and Torres Strait Islander people. The framework will be a synthesis of evidence that represents best practices in eHealth, as determined by Aboriginal and Torres Strait Islander people. METHODS: Research activities to develop the best practice framework will occur in stepped but overlapping qualitative research phases with governance from an existing multiagency research collaboration (the Collaboration). The research protocol has been informed by key research frameworks such as the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and Developers of Health Research Reporting Guidelines. The seven phases of research will include the following: systematic literature review, scoping review, theme development, theme consultation, Delphi processes for expert reviews, and dissemination. RESULTS: Members of the Collaboration conceived this research program in August 2020, and a draft was produced in June 2021 with subsequent funding obtained in July 2021. The Collaboration approved the protocol in December 2021. Results for several research phases of the best practice framework development are expected by January 2023, commencing with the systematic literature review and the scoping review. CONCLUSIONS: The research program outlined in this protocol is a timely response to the growing number of eHealth interventions with Aboriginal and Torres Strait Islander people. A best practice framework is needed to guide the rigorous development and evaluation of eHealth innovations to promote genuine co-design and ensure cultural safety and clinical effectiveness for Aboriginal and Torres Strait Islander people. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34904.
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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 | 0.212 | 0.197 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.054 | 0.016 |
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".