Citizen science as an approach for engaging underrepresented communities in codevelopment of health research
Bibliographic record
Abstract
OBJECTIVES: Engaging patients and public in health research ensures results remain relevant and responsive to community needs. However, meaningful engagement with underrepresented communities remains challenging, and this lack of representation can perpetuate ongoing inequities in health research. Citizen science offers a flexible methodological approach to prioritize active and meaningful patient and public engagement, including from underrepresented communities, where community voices are included throughout the research process. This commentary explores how a citizen science approach can be applied to enhance engagement of underrepresented communities and support co-developing research questions that reflect community needs. STUDY DESIGN AND SETTING: We present a case example of an ongoing project working with 2S/LGBTQQIA+ communities to codevelop a health research program centered around chronic pain using citizen science. The project includes a nationwide online platform, group workshops, and consensus approaches, with activities guided by an Advisory Committee of 2S/LGBTQQIA+ individuals. We describe the European Citizen Science Association's ten principles of citizen science, their application in the example . RESULTS: The case example demonstrates how citizen science can be used to codevelop a research program that reflects community needs, balancing large-scale public engagement with collaborative cocreation. Prioritizing active community engagement throughout the research process promotes transparency and inclusion, setting a new standard for collaborative research. CONCLUSION: Citizen science holds significant potential for broader application across diverse health domains, offering an innovative alternative to traditional methods of priority-setting, and provides a flexible framework for engaging underrepresented communities to advance health equity.
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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 | 0.333 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.036 | 0.083 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.008 | 0.068 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".