E-Mental Health Innovations for Aboriginal and Torres Strait Islander Australians: A Qualitative Study of Implementation Needs in Health Services
Bibliographic record
Abstract
BACKGROUND: Electronic mental health (e-mental health) interventions offer effective, easily accessible, and cost effective treatment and support for mental illness and well-being concerns. However, e-mental health approaches have not been well utilized by health services to date and little is known about their implementation in practice, particularly in diverse contexts and communities. OBJECTIVE: This study aims to understand stakeholder perspectives on the requirements for implementing e-mental health approaches in regional and remote health services for Indigenous Australians. METHODS: Qualitative interviews were conducted with 32 managers, directors, chief executive officers (CEOs), and senior practitioners of mental health, well-being, alcohol and other drug and chronic disease services. RESULTS: The implementation of e-mental health approaches in this context is likely to be influenced by characteristics related to the adopter (practitioner skill and knowledge, client characteristics, communication barriers), the innovation (engaging and supportive approach, culturally appropriate design, evidence base, data capture, professional development opportunities), and organizational systems (innovation-systems fit, implementation planning, investment). CONCLUSIONS: There is potential for e-mental health approaches to address mental illness and poor social and emotional well-being amongst Indigenous people and to advance their quality of care. Health service stakeholders reported that e-mental health interventions are likely to be most effective when used to support or extend existing health services, including elements of client-driven and practitioner-supported use. Potential solutions to obstacles for integration of e-mental health approaches into practice were proposed including practitioner training, appropriate tool design using a consultative approach, internal organizational directives and support structures, adaptations to existing systems and policies, implementation planning and organizational and government investment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".