Cultural interventions that target mental health and wellbeing for First Nations Australians: a systematic review
Bibliographic record
Abstract
Objective: The continuity of Australian First Nations (Aboriginal and Torres Strait Islander) culture has been threatened by colonisation and effects of this continue to have devastating impacts on their social emotional wellbeing [SEWB], especially mental health. This review analyses cultural interventions aiming to improve mental health outcomes for First Nations Australians (e.g., mood, self-esteem, suicide-attempts, self-harm, risky behaviours) to uncover the effectiveness and key components of such interventions. Method: Databases PsycINFO, CINAHL, EMBASE, EMCARE, LIt.search tool from Lowitja Inst, Australian Indigenous Health InfoNet and Google Scholar were searched. Studies published between 2000 and 2021 which reported the impact of cultural interventions on the mental health of First Nations Australians were included. Results: From 172 studies, only eight studies met inclusion criteria and all improved measured domains of SEWB. Six studies evaluated culturally adapted interventions (i.e., Western interventions adapted to be culturally appropriate) and two evaluated culturally grounded interventions (i.e., interventions developed by First Nations Australians). Participants called for more cultural components in culturally adapted interventions. The most successful studies used collaborative and participatory approaches in the designs, included First Nations members in their research teams and presented culturally grounded interventions. Conclusions: The paucity of literature limit findings. There was a limited ability to identify key mechanisms of change across some intervention studies, and large outcome variations across studies meant some aspects could not be compared. Nonetheless, this review concludes that culturally grounded interventions are the most promising and successful mental health interventions currently available for First Nations Australians which has many implications for practice and funding.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".