Academic accountability to local communities and society through Programme Science: a case study from the HPV self‐sampling programme HOPE in Peru
Bibliographic record
Abstract
INTRODUCTION: Health challenges in the 21st century underscore the need for adaptable and innovative approaches in public health. Academic institutions can and should contribute much more effectively to generate and translate scientific knowledge that will result in better programmes to improve societal health. Academic accountability to local communities and society requires universities to actively engage with local communities, understanding the context, their needs, and leveraging their knowledge and local experience. The Programme Science initiative provides a framework to optimize the scale, quality and impact of public health programmes, by integrating diverse approaches during the iterative cycle of research and practice within the strategic planning, programme implementation and programme management and evaluation. We illustrate how the Programme Science framework could be a useful tool for academic institutions to accomplish accountability to local communities and society through the experience of Project HOPE in Peru. DISCUSSION: Project HOPE applied the Programme Science framework to introduce HPV self-sampling into a women's health programme in Peru. Collaboration with local authorities and community members was pivotal in all phases of the project, ensuring interventions aligned with community needs and addressing social determinants of health. The HOPE Ladies-community women trained and empowered to promote and provide the HPV kits-crafted the messages used through the study and developed strategies to reach individuals and provided support to women's journey through health centres. By engaging communities in co-creating knowledge and addressing health inequities, academic institutions can generate contextually relevant and socially just scientific knowledge. The active participation of community women in Project HOPE was instrumental in improving service utilization and addressing barriers to self-sampling. CONCLUSIONS: The Programme Science approach offers a pathway for academic institutions to enhance their accountability to communities and society at large. By embedding researchers within public health programmes and prioritizing community engagement, academic institutions can ensure that research findings directly inform policy improvements and programmatic decisions. However, achieving this requires a realignment of research agendas and recognition of the value of community engagement. Establishing Programme Science networks involving academia, government and funding entities can further reinforce academic accountability and enhance the impact of public health 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".