Citizen Science for Health: An International Survey on Its Characteristics and Enabling Factors
Bibliographic record
Abstract
Even though citizen and patient engagement in health research has a long tradition, citizen science in health has only recently gained attention and recognition. However, at present, there is no clear overview of the specifics and challenges of citizen science initiatives in the health domain. Such an overview could contribute to highlighting and articulating the different needs of stakeholders engaged in any form of citizen science in the health domain. It may also encourage the input of citizens and patients alike in health research and innovation, policy, and practice. This paper reports on a survey developed by the European Citizen Science Association (ECSA)’s Working Group “Citizen Science for Health,” to highlight the perceived characteristics and enabling factors of citizen science in the health domain, and to formulate a direction for future work and research. The survey was available in six languages and was open between January and August 2022. The majority of the 254 respondents were from European countries, and the largest stakeholder respondent group was researchers. Respondents were asked about their perspectives on the particular characteristics of citizen science performed in health and biomedical research, as well as the challenges and opportunities it affords. Ethics, the complexity of the health domain, and the overlap in roles whereby the researcher is sometimes also the subject of research, were the main issues suggested as being specific to citizen science in health. The top two areas that respondents identified as in need of development were “balanced return on investment” and “ethics.” This publication discusses these and other conditions with references to current literature.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".