What do women living with HIV think about research on children born HIV-free in the UK?
Bibliographic record
Abstract
ObjectiveWe are analysing linked public health surveillance data on pregnancies to women living with HIV in the UK and vital registration data on all their children born HIV-free, an under-researched group. We sought to engage women living with HIV to elicit their feedback on our work and set research priorities. ApproachIn partnership with 4M Net, a national peer-support network for mothers living with HIV, we co-designed and held two online workshops (one workshop a week over two weeks) in March 2022. We designed the workshops to be highly interactive, using a combination of online tools and created a safe space for open discussion. All participants received a preparatory booklet in advance, comprising questions, activities, and space for reflections during the workshops. We also prompted participants to discuss their research priorities with peers and/or family members if they felt comfortable with this. ResultsIn total, 6 participants attended the two 2-hour long workshops. All participants were from Black ethnic backgrounds, aged 30 years or older and were mothers of children or young adults born HIV-free. Overall, participants were positive about the programme of research and identified pregnancy, birth, and long-term outcomes in children (especially regarding HIV medication in pregnancy) as being of key importance. They supported the use of linked mental health, hospital, general practice (GP), health visiting and education data to explore pregnancy, health and developmental outcomes among children born HIV-free. Linkage to GP data was identified as a priority by participants, and there was particular interest in addressing knowledge gaps on the mental health of children and young adults born HIV-free. ConclusionWomen living with HIV are often involved in research around pregnancy, but rarely in research about their children. By working with trusted community partners, we can engage this often marginalised group of parents who understand the value of linked data research exploring the health and wellbeing of their children born HIV-free.
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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 | 0.116 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".