Patients as Partners in Sickle Cell Disease Research in Africa: A Framework for Equitable Patient-Engaged Health Research
Bibliographic record
Abstract
BACKGROUND: Patient and public involvement (PPI) in health research is gaining global momentum through initiatives such as INVOLVE (UK), the Patient-Centred Outcomes Research Institute (USA), and the Strategy for Patient-Oriented Research (Canada). However, its implementation in Africa remains limited and lacks context-specific guidance. To address this gap, the Sickle Africa Data Coordinating Center supported the development of the Patients as Partners in Sickle Cell Disease Research (SCD-PAPIR) Framework to guide meaningful patient engagement and involvement in SCD research in Africa. METHODS: An iterative, participatory approach was adopted. The process involved the creation of a SCD PAPIR working group comprising SCD patient advocates in 14 African countries; 18 virtual working group meetings, one public webinar, and three in-person workshops. The framework was co-created through facilitated discussions reflecting on prior engagement in SCD research as a patient partner, and best practices for effective patient-researcher collaborations. Patient-only sessions and leadership roles were integrated to ensure safe spaces and to minimise power imbalances. RESULTS: The SCD-PAPIR Framework positions SCD patients and their caregivers as citizen researchers. Key to the framework is the principle of subsidiarity, which affirms the agency and experiential knowledge of patients while calling for epistemic humility from researchers. Its core pillars include valuing patient expertise, transparent communication, resource sharing, patient empowerment, collective learning, amplification of patient voice, continuous feedback, and shared benefits. Best practices emphasise two-way communication, addressing power asymmetries, co-learning and patient empowerment, co-ownership of outputs, and formalised PAPIR structures. CONCLUSION: The SCD-PAPIR Framework provides a contextually grounded model for patient-engaged research in Africa and contributes to efforts to decolonise health research by positioning patients as co-creators of knowledge, and not merely a data source. The effective implementation of the framework will require investment in institutionalising PAPIR in SCD research. Future work should focus on designing implementation toolkits, developing PPI training modules for researchers and patient advocates, and adapting the framework to other health conditions.
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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 | 0.350 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.028 | 0.099 |
| Scholarly communication | 0.037 | 0.046 |
| Open science | 0.009 | 0.062 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 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".