Global Learning Opportunities Within Social Innovation in Health (GLOWS): Modified Delphi Process to Identify and Pilot Core Competencies for Learning
Bibliographic record
Abstract
Abstract Background Social innovation in health refers to the community-engaged process that connects health improvement and social change. The aim of this study was to develop a consensus statement on core learning competencies in social innovation in health and pilot them as part of a participatory training workshop. Methods and Findings A modified Delphi Process aggregating data from a scoping review, global open call, and participatory process was organized. Participants were recruited from low, middle, and high-income countries with a range of social innovation experiences. Statements focused on social innovation in health core competencies for learning. Consensus was determined using the RAND/UCLA Appropriateness method. After expressing interest in the project, 68 individuals received the survey link. 46 participants completed the first survey, and 34 completed the second survey. All 28 statements reached consensus, and based on the results of this first survey, some statements were added, amended, and merged to reach 30 consensus statements in the second survey. Competencies were categorized into skills, mindsets, and knowledge. Some competencies reached higher levels of agreement than others. This included community engagement, which can leverage the collective knowledge and problem-solving abilities of a diverse group of individuals to tackle complex challenges; social entrepreneurship skills such as business model knowledge, securing funding, team building, and knowledge of intersectional issues and health inequities. Several learning competencies were then piloted as eight one-hour online workshops, which assessed the feasibility of developing them through online open-access social innovation training sessions. After completing the workshops, 137 participants completed a survey, and most participants reported a significant improvement across six competencies. Conclusion The results from this study will inform the development of a WHO/TDR conceptual framework for teachers and learners in social innovation in health. Author Summary Why was this study done? This study was undertaken to develop a consensus statement on core learning competencies in social innovation in health and pilot them as part of a participatory training workshop. What did the researchers do and find? Some of the core competencies that reached high levels of agreement amongst the international panel included community engagement, which can leverage the collective knowledge and problem-solving abilities of a diverse group of individuals to tackle complex challenges; social entrepreneurship skills such as business model knowledge, securing funding, team building, and knowledge of intersectional issues and health inequities. What do these findings mean? These findings are important for fostering social innovation in health training programmes and will inform the development of a WHO/TDR conceptual framework.
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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 | 0.122 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".