Developing a matrix to identify and prioritise research recommendations in HIV Prevention
Bibliographic record
Abstract
HIV prevention continues to be problematic in the UK, as it does globally. The UK Department of Health has a strategic direction with greater focus on prevention as part of its World Class Commissioning Programme. There is a need for targeted evidence-based prevention initiatives. This is an exploratory study to develop an evidence mapping tool in the form of a matrix: this will be used to identify important gaps in contemporary HIV prevention evidence relevant to the UK. It has the potential to aid prioritisation in future research. Categories for prevention and risk groups were developed for HIV prevention in consultation with external experts. These were used as axes on a matrix tool to map evidence. Systematic searches for publications on HIV prevention were undertaken using electronic databases for primary and secondary research undertaken mainly in UK, USA, Canada, Australia and New Zealand, 2006-9. Each publication was screened for inclusion then coded. The risk groups and prevention areas in each paper were counted: several publications addressed multiple risk groups. The counts were exported to the matrix and clearly illustrate the concentrations and gaps of literature in HIV prevention. 716 systematic reviews, randomised control trials and other primary research met the inclusion criteria for HIV prevention. The matrix identified several under researched areas in HIV prevention. This is the first categorisation system for HIV prevention and the matrix is a novel tool for evidence mapping. Some important yet under-researched areas have been identified in HIV prevention evidence: identifying the undiagnosed population; international adaptation; education; intervention combinations; transgender; sex-workers; heterosexuals and older age groups. Other research recommendations: develop the classification system further and investigate transferability of the matrix to other prevention areas; evidence syntheses may be appropriate in areas dense with research; have studies with positive findings been translated to practice? The authors of this study invite research suggestions relating to the evidence gaps identified within remits of Public Health or any appropriate NETSCC programme. Follow the 'Suggest Research' links from: http://www.netscc.ac.uk/ . Enter - HIVProject - in optional ID for HTA or in first information box for other programmes.
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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 | 0.124 | 0.317 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.054 | 0.040 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.022 | 0.023 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".