Lessons for Patient Engagement in Research in Low- and Middle-Income Countries
Bibliographic record
Abstract
Patient engagement in research is marked by partnership between clinicians, scientists, and people with lived experience of a disease, who jointly develop and implement research and disseminate results. Patient engagement in research has been shown to lead to more impactful and relevant findings. There is a global need for quality research contextualized for low- and middle-income countries (LMICs). Patient involvement in research could address this need, yet it remains a practice more commonly employed in high income countries. In this paper, the authors explore LMIC-specific challenges and opportunities for patient engagement in research. Limitations to patient engagement in research include gaps in health infrastructure, socioeconomic status, cultural stigma, and uncertain roles. Potential solutions to address these challenges include strategic national and international research partnerships, initiatives to combat stigma, and sensitization and training of stakeholders in patient engagement in research. Reflecting on their patient engagement experience with eye cancer research in Canada and Kenya, and supported by evidence of patient engagement in other low-resource settings, the authors provide a roadmap for patient engagement in research in LMICs.
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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 | 0.119 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".