From Understanding to Impactful Action: Systems Thinking for Systems Change in Chronic Disease Prevention Research
Bibliographic record
Abstract
Within the field of chronic disease prevention, research efforts have moved to better understand, describe, and address the complex drivers of various health conditions. Change-making is prominent in this paper, and systems thinking and systems change are prioritised as core elements of prevention research. We report how the process of developing a theory of systems change can assist prevention research to progress from understanding systems, towards impactful action within those systems. Based on Foster-Fishman and Watson’s ABLe change framework, a Prevention Systems Change Framework (PSCF) was adapted and applied to an Australian case study of the drivers of healthy and equitable eating as a structured reflective practice. The PSCF comprises four components: building a systemic lens on prevention, holding a continual implementation focus, integrating the systemic lens and implementation focus, and developing a theory of change. Application of the framework as part of a systemic evaluation process enabled a detailed and critical assessment of the healthy and equitable eating project goals and culminated in the development of a theory of prevention systems change specific to that project, to guide future research and action. Arguably, if prevention research is to support improved health outcomes, it must be more explicitly linked to creating systems change.
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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 | 0.083 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".