Leadership for systems change: Researcher practices for enhancing research impact in the prevention of chronic disease
Bibliographic record
Abstract
Introduction: Strengthening systems for chronic disease prevention is essential. Leadership for systems change is an important key to strengthening systems. Leadership in prevention research for supporting systems change remains a relatively abstract concept and there is limited empirical information about the leadership practices of prevention research teams when viewed through a complexity lens. In this paper we examine and describe some systems leadership practices for creating change through prevention research, as identified in a series of six case studies. Methods: A qualitative approach incorporating semi-structured interviews, participant observation, and document review was used to facilitate an in-depth investigation of the research topic. Results: Several researcher practices for enhancing research impact in the prevention of chronic disease were distilled from the data pertaining to how they sought to create change. These included persuasive communication, compassion and deep listening, reflective practice, and embedding themselves within the systems they sought to change. Discussion: The findings provide insights that may assist prevention researchers and other practitioners dedicated to creating change in chronic disease prevention.
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 | 0.334 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".