Troubling the boundaries: Overcoming methodological challenges in a multi-sectoral and multi-jurisdictional HIV/HCV policy scoping review
Bibliographic record
Abstract
Policy scoping reviews are an effective method for generating evidence-informed policies. However, when applying guiding methodological frameworks to complex policy evidence, numerous, unexpected challenges can emerge. This paper details five challenges experienced and addressed by a policy trainee-led, multi-disciplinary research team, while conducting a scoping review of youth Human Immunodeficiency Virus and Hepatitis C primary and secondary prevention policies, which occurred across and within sectors and jurisdictions in Atlantic Canada. How these challenges were addressed is described, as are suggestions for how the lessons learned may provide guidance to other policy scoping reviews. Implications for future directions are also discussed.
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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 | 0.666 | 0.709 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".