The Effectiveness, Facilitators, and Barriers of Digital Mental Health Services for First Nations People in Australia: Systematic Scoping Review
Bibliographic record
Abstract
BACKGROUND: First Nations peoples in Australia experience inequitable mental health outcomes and service access. Digital mental health (DMH) services, which refer to offering mental health services through digital platforms, are considered potential solutions to address such mental health service inequities and improve First Nations Australians' mental health outcomes. However, evidence on the effectiveness of DMH for First Nations Peoples in Australia is yet to be synthesised. OBJECTIVE: This systematic scoping review aimed to fill this gap, and to identify the facilitators and barriers that influence the implementation of DMH services in this context. METHODS: A systematic search was conducted across six academic databases to search for studies related to DMH services for First Nations Peoples in Australia. Search terms relating to First Nations Peoples, geographic terminologies of Australia, mental health, and digital mental health services were used. Studies were included if they assessed the effectiveness, or determinants of facilitators and barriers of implementing digital mental health interventions among First Nations people in Australia. Data were extracted based on study design, targeted services, and research findings, then synthesised using a thematic analysis framework. RESULTS: In total, 22 studies met the inclusion criteria. DMH services were used to provide support, treatment, and psychological assessments for First Nations Australians. Evidence of effectiveness was stronger for non-severe mental health conditions. Determinants of facilitators and barriers of the implementation of DMH services included: (i) organisational and administrative factors; (ii) cultural appropriateness; (iii) accessibility; (iv) integration of DMH services to the existing health system; (v) engagement between clients and service providers; (vi) coverage of different conditions and clients; (vii) acceptability to DMH services; (viii) digital literacy, and (ix) efficiency. CONCLUSIONS: Evidence on the use of digital mental health (DMH) services for First Nations Australians remains heterogeneous in study design and outcome measurement. DMH services appear most effective for managing non-severe mental health conditions. Successful implementation requires multi-level structural support, including policy and organisational commitment, enhanced digital infrastructure, workforce training and engagement, and the design of culturally responsive DMH models to improve uptake and equitable access to mental health care among First Nations Australians.
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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 | 0.024 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".