Diversity in eMental Health Practice: An Exploratory Qualitative Study of Aboriginal and Torres Strait Islander Service Providers
Bibliographic record
Abstract
BACKGROUND: In Australia, mental health services are undergoing major systemic reform with eMental Health (eMH) embedded in proposed service models for all but those with severe mental illness. Aboriginal and Torres Strait Islander service providers have been targeted as a national priority for training and implementation of eMH into service delivery. Implementation studies on technology uptake in health workforces identify complex and interconnected variables that influence how individual practitioners integrate new technologies into their practice. To date there are only two implementation studies that focus on eMH and Aboriginal and Torres Strait Islander service providers. They suggest that the implementation of eMH in the context of Aboriginal and Torres Strait Islander populations may be different from the implementation of eMH with allied health professionals and mainstream health services. OBJECTIVE: The objective of this study is to investigate how Aboriginal and Torres Strait Islander service providers in one regional area of Australia used eMH resources in their practice following an eMH training program and to determine what types of eMH resources they used. METHODS: Individual semistructured qualitative interviews were conducted with a purposive sample of 16 Aboriginal and Torres Strait Islander service providers. Interviews were co-conducted by one indigenous and one non-indigenous interviewer. A sample of transcripts were coded and thematically analyzed by each interviewer and then peer reviewed. Consensus codes were then applied to all transcripts and themes identified. RESULTS: It was found that 9 of the 16 service providers were implementing eMH resources into their routine practice. The findings demonstrate that participants used eMH resources for supporting social inclusion, informing and educating, assessment, case planning and management, referral, responding to crises, and self and family care. They chose a variety of types of eMH resources to use with their clients, both culturally specific and mainstream. While they referred clients to online treatment programs, they used only eMH resources designed for mobile devices in their face-to-face contact with clients. CONCLUSIONS: This paper provides Aboriginal and Torres Strait islander service providers and the eMH field with findings that may inform and guide the implementation of eMH resources. It may help policy developers locate this workforce within broader service provision planning for eMH. The findings could, with adaptation, have wider application to other workforces who work with Aboriginal and Torres Strait Islander clients. The findings highlight the importance of identifying and addressing the particular needs of minority groups for eMH services and resources.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".