Using a Nominal Group Technique to Develop a Science Communication Curriculum for Health Professionals and Clinical Researchers
Bibliographic record
Abstract
Effective science communication is fundamental to closing the gap from research and innovation to clinical implementation. Existing paradigms of science communication are often challenged by a lack of skill and engagement, particularly from those who progress the science. Currently, a standardized curriculum on science communication, with global applicability, does not exist. The purpose of this project is to address the gap in training by health professionals and clinical researchers through the development of a globally relevant curriculum for science communication. The nominal group technique (NGT) was used whereby a convenience sample of eleven science communication experts from across the globe generated, discussed, and arrived at a consensus on topics that should be included in a standardized science communication curriculum. Experts represented diverse backgrounds within the health sciences. Due to the COVID-19 pandemic and geographical constraints, the NGT was conducted virtually. The consensus-building methodology allowed for each expert to equally present ideas and collaborate with one another to create a robust and comprehensive curriculum for effective science communication. Expert panelists reached a consensus on 10 essential components of a standardized global science communication curriculum. Following the refinement of the curriculum topic areas, a virtual meeting with project co-investigators was held to review the topics and discuss relevance, applicability, and appeal to the local contexts. A standardized science communication curriculum is needed for health professionals and clinical researchers. The NGT achieved expert consensus on the core topics. The next steps are to develop the course ensuring optimal participation from learners across the globe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.127 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".