Achieving better engagement with care and support for young people living with HIV in Australia: a mixed-method enquiry
Bibliographic record
Abstract
Young people aged 18-29 are considered "adult" within the Australian HIV health service context. However, evidence increasingly defines this age group as distinct from the broader adult population such that the needs of young people living with HIV may be overlooked in the context of HIV service design and delivery. This analysis draws on the Young + Positive study, a national study in Australia that documented the perspectives of young people (aged 18-29) living with HIV. Data were collected via survey (n = 60) and interview (n = 25) methods between 2018 and 2019. The data were analysed using descriptive statistics and thematic analysis, exploring the inner- and outer-world factors influencing participant engagement with HIV care and support. Using the multi-dimensional framework by Harms [2021. Understanding human development (3rd ed.). Oxford University Press], we found that both inner- and outer-world factors influenced participants' ability and motivations to engage with specialist HIV treatment and support. Inner-world factors included psychological outlook, and perceptions of HIV and HIV services. Outer-world factors included workforce competencies of service providers, physical space of the service and hours of service operation. These research findings confirm that opportunities exist to better meet the treatment and care needs of young people living with HIV.
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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 | 0.056 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 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".