Use and uptake of web-based therapeutic interventions amongst Indigenous populations in Australia, New Zealand, the United States of America and Canada: a scoping review
Bibliographic record
Abstract
BACKGROUND: Barriers to receiving optimal healthcare exist for Indigenous populations globally for a range of reasons. To overcome such barriers and enable greater access to basic and specialist care, developments in information and communication technologies are being applied. The focus of this scoping review is on web-based therapeutic interventions (WBTI) that aim to provide guidance, support and treatment for health problems. OBJECTIVES: This review identifies and describes international scientific evidence on WBTI used by Indigenous peoples in Australia, New Zealand, Canada and USA for managing and treating a broad range of health conditions. ELIGIBILITY CRITERIA: Studies assessing WBTI designed for Indigenous peoples in Australia, Canada, USA and New Zealand, that were published in English, in peer-reviewed literature, from 2006 to 2018 (inclusive), were considered for inclusion in the review. Studies were considered if more than 50% of participants were Indigenous, or if results were reported separately for Indigenous participants. SOURCES OF EVIDENCE: Following a four-step search strategy in consultation with a research librarian, 12 databases were searched with a view to finding both published and unpublished studies. CHARTING METHODS: Data was extracted, synthesised and reported under four main conceptual categories: (1) types of WBTI used, (2) community uptake of WBTI, (3) factors that impact on uptake and (4) conclusions and recommendations for practice. RESULTS: A total of 31 studies met the inclusion criteria. The WBTI used were interactive websites, screening and assessment tools, management and monitoring tools, gamified avatar-based psychological therapy and decision support tools. Other sources reported the use of mobile apps, multimedia messaging or a mixture of intervention tools. Most sources reported moderate uptake and improved health outcomes for Indigenous people. Suggestions to improve uptake included as follows: tailoring content and presentation formats to be culturally relevant and appropriate, customisable and easy to use. CONCLUSIONS: Culturally appropriate, evidence-based WBTI have the potential to improve health, overcome treatment barriers and reduce inequalities for Indigenous communities. Access to WBTI, alongside appropriate training, allows health care workers to better support their Indigenous clients. Developing WBTI in partnership with Indigenous communities ensures that these interventions are accepted and promoted by the communities.
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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 | 0.031 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".