Enhancing Aboriginal and Torres Strait Islander young people's resilience to blood-borne and sexually transmitted infections: findings from a community-based participatory research project
Bibliographic record
Abstract
Issue addressed Health services are fundamental to reducing the burden of blood-borne and sexually transmitted infections (BBV/STI) in Indigenous communities. However, we know very little about young Indigenous people's use of mainstream and community-controlled health services for the prevention and treatment of these infections, or how health services can best support young people's efforts to prevent infection. Methods University-researchers, a site co-ordinator and peer researchers developed a project and conducted interviews with 45 Aboriginal and Torres Strait Islander people aged between 17 and 26 years. Thematic analysis of interview notes identified key themes around health service use and experiences of Aboriginal Community-Controlled Health Services (ACCHS). Results Most participants had accessed health services for the prevention or treatment of BBV/STI, with positive experiences characterised by the provision of information and feeling cared for. Participants described the comfort and understanding they experienced at ACCHS; personal relationships and having an Indigenous care provider present were important factors in the overwhelmingly positive accounts. Young people reported strategies for overcoming challenges to prevention and accessing treatment services, including being proactive by carrying condoms and persisting with behavioural intentions despite feeling shame. Conclusion Our findings reinforce the important role both mainstream and community-controlled health services have in the prevention and treatment of blood-borne and sexually transmitted infections in young Indigenous people. We highlight opportunities to build on young people's strengths, such as their valuing of their health, their persistence, and their offers to support peers, to better prevent transmission of infections and enhance access to treatment. So what Providing culturally appropriate health services to young Indigenous people is crucial to early detection and treatment of blood-borne and sexually transmitted infections. Health services need to listen to young Indigenous people's perspectives and build on their strengths to provide prevention and treatment programs that are designed and delivered with their cultural and health needs in mind.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".