An evidence review from Australia, Aotearoa/New Zealand and Canada for the WA Aboriginal Sexual Health and Bloodborne Viruses Strategy
Bibliographic record
Abstract
This review was commissioned by the Department of Health WA as part of the development process for the fourth WA Aboriginal sexual health and blood-borne virus (SHBBV) strategy (2019–2023). This strategy aims to prevent and reduce rates of infection in Australian communities.This review has followed the structure of the third strategy, analysing the data through each domain: prevention and education; testing and diagnosis; disease management and clinical care; workforce development; enabling environments, and; research, evaluation, and surveillance. The review aims to provide a broad overview of evidence on the way SHBBV health promotion, care, and research are negotiated with Aboriginal populations across three countries: Aotearoa/ New Zealand, Australia, and Canada. The data were collected through a scoping review of academic and grey literature (2005-2018), and further developed with the guidance of an advisory group. Culturally appropriate reviews can provide inroads to more constructive policy-making and research practices in health [45, 46]. Reviews can help to address the gaps in inclusive practice guidelines that are evident in current Australian Aboriginal health strategies, in part due to significant knowledge gaps [47]. In particular, reviews which utilise ‘decolonising methodologies’ (such as stakeholder consultation and the expansion of what can be considered ‘valid’ knowledge) can include research that is informed by the needs of communities, as well as the data collected through standard literature searches [48]. Decolonisation, the positioning of Aboriginal-led frameworks at the fore of service delivery, is a crucial aspect of positive and constructive health care, promotion, and research for Aboriginal people [49-51]. It involves directly engaging affected communities, keeping Aboriginal voices amplified and considered throughout institutional processes, practicing reflexivity and immersion, and ensuring that any research or health care/promotion outcomes are addressed with an Aboriginal lens in mind [51-53].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".