A community‐led design for an Indigenous Model of Mental Health Care for Indigenous people with depressive disorders
Bibliographic record
Abstract
OBJECTIVE: To generate outcomes for the development of a culturally appropriate mental health treatment model for Indigenous Australians with depression. METHODS: Three focus group sessions and two semi-structured interviews were undertaken over six months across regional and rural locations in South West Queensland. Data were transcribed verbatim and coded using manual thematic analyses. Transcripts were thematically analysed and substantiated. Findings were presented back to participants for authenticity and verification. RESULTS: Three focus group discussions (n=24), and two interviews with Elders (n=2) were conducted, from which six themes were generated. The most common themes from the focus groups included Indigenous autonomy, wellbeing and identity. The three most common themes from the Elder interviews included culture retention and connection to Country, cultural spiritual beliefs embedded in the mental health system, and autonomy over funding decisions. CONCLUSIONS: A treatment model for depression must include concepts of Indigenous autonomy, identity and wellbeing. Further, treatment approaches need to incorporate Indigenous social and emotional wellbeing concepts alongside clinical treatment approaches. Implications for public health: Any systematic approach to address the social and cultural wellbeing of Indigenous peoples must have a community-led design and delivery.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".