Can We Build an Evidence Base on the Impact of Systems Thinking for Wicked Problems? Comment on "What Can Policy-Makers Get Out of Systems Thinking? Policy Partners’ Experiences of a Systems-Focused Research Collaboration in Preventive Health"
Bibliographic record
Abstract
The published literature on the application of systems thinking to influence policies and programs has grown in recent years. The original article by Haynes et al and the subsequent commentaries have focused on the upstream connection between capacity building for systems thinking and systems informed decision-making. This commentary explores the downstream connection between systems-informed decision-making and broader impacts on the health system, the health of the population and other economic and social benefits. Storytelling, systems-based syntheses and systems intervention principles are explored as approaches to strengthen the evidence base. For systems thinking to gain broader acceptance and application to complex health-related challenges, we need more of an evidence base demonstrating impact.
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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 | 0.042 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.087 | 0.091 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".