The Ripple Effect, Silence and Powerlessness: Hidden Barriers to Discussing Suicide in Australian Aboriginal Communities
Bibliographic record
Abstract
Abstract Background Suicide is one of the leading causes of death for Aboriginal Australians. There is an urgent need to actively engage with Aboriginal communities to better understand these issues and to develop solutions together to prevent deaths by suicide in Aboriginal communities. Methods Utilising a qualitative, thematic, cross-sectional design, we conducted focus groups in three communities in New South Wales (Australia) to explore the perceptions and views of Aboriginal participants in relation to discussing suicide. Results The key themes found to influence discussions about suicide in Aboriginal communities included the sense that suicide is a whole of community issue, the ripple effect of suicide deaths, silence about suicide and the impact of this silence, and being powerless to act. Participants described a reluctance to have discussions about suicide; feeling they had limited skills and confidence to have these sorts of discussions; and multiple and interrelated barriers to discussing suicide, including shame, fear and negative experiences of mental health care. Participants also described how their experiences maintained these barriers and prevented Aboriginal Australians from seeking help in suicidal crises. Conclusion Future initiatives should address the interrelated barriers by supporting Aboriginal people to build skills and confidence in discussing suicide and mental health and by improving access to, and the experience of, mental health care and psychosocial and community-based supports for Aboriginal Australians. We suggest trying to address any one of these factors in isolation may increase rather than decrease suicide risk in Aboriginal communities.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".