The Participatory Zeitgeist in Health Care: It is Time for a Science of Participation
Bibliographic record
Abstract
Participation in health care is currently the zeitgeist/spirit of our times. A myriad of practices characterizes this "participatory Zeitgeist" in contemporary health care, which range from patients and professionals collaborating as partners in service delivery and treatment decision-making, to crowdsourced cures and participation in online communities, to using health apps, to involvement in health care quality improvement initiatives for systems redesign using coproduction and co-design methods. To date, patient engagement and participation in online communities and the use of apps have received a good deal of attention in participatory medicine. However, there has been a less critical examination of participation in health care planning, design, delivery, and improvement. In the face of what Thomas Kuhn called a scientific revolution, we are presented with the opportunity to re-examine some of the assumptions underpinning participation in health care and some of the emerging anomalies and weaknesses in the current science. This re-examination will allow the development of a new paradigm, a science of participation. In this science, we can systematically test, refine, and advance participation in health care to build a unifying language and theories from across the interdisciplinary fields of participatory design, medicine, and research to develop and test models to explain impacts and outcomes. A science of participation will allow the emergent and unexplained facts to be addressed in the current participatory mood of health care planning, design, delivery, and improvement.
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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 | 0.061 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.169 |
| Scholarly communication | 0.023 | 0.033 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 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".