System tools for system change
Bibliographic record
Abstract
BACKGROUND: Health system transformations are influenced by dynamic relationships within and between individuals and institutions, as well as political, educational and legislative factors. This article aims to promote awareness of five tools that recognise this complexity and that are proposed to have value for decision makers: concept mapping, social network analysis, system dynamics modelling, programme budgeting and marginal analysis, and the tools for knowledge management and translation. METHODS: The authors briefly describe the methodological approach of each tool, provide a commentary on the conditions in which these tools have been employed, and discuss their impact on the processes and outcomes of system transformation. An international advisory panel was convened based on a combination of experience, expertise and perspective. The panel assisted in synthesising the evidence relating to each tool and, in partnership with the authors, refined the interpretation of the role and value of each tool for system transformation. FINDINGS: The tools discussed may impact the structural and procedural outcomes of transformation as well as the values, behaviours and attitudes of people undergoing change. The techniques described provide those undertaking transformation with methods to negotiate clinical, academic, political, organisational and cultural perspectives, and recognise the pivotal role of context in transformation. CONCLUSIONS: This review offers a novel synthesis of how these tools may add value to decision making for health policy. The tools discussed, while not a panacea to the challenges of large system change, provide methods that acknowledge the complexity of the transformative challenge and present innovative paths to co-produced solutions.
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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 | 0.048 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.012 |
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".