A Six-Case Comparative Study to Identify Systems Thinking in Government-designed Public Health Strategies
Bibliographic record
Abstract
Governments around the world are grappling with increasingly complex and hard to solve public health problems, such as the rise in chronic diseases and increasing health inequity. Complexity is a significant challenge for policy makers who design large-scale interventions for several reasons: the interconnected dynamics of a system might lead policy design to overlook potential synergies, the heterogeneity of factors and the nonlinearity of processes in a system make causal links difficult to determine, and prediction is difficult because multiple forces shape the future behaviour of the system. Traditional theoretical frameworks, such as the biomedical or behavioural health models, are based on a reductionist paradigm that holds that all phenomena can be understood by reducing them to their smallest components and studying their interactions. This reductionist stance is inadequate for dealing with increasing complexity in public health. In this dissertation, I explore the use of systems thinking as a theoretical framework for understanding complex public health issues and designing government-led public health strategies. A systems thinking framework considers how dynamic feedback, unpredictability, and interconnected elements impact the overall structures and patterns in a system. I use a multiple case study analysis to investigate the extent to which systems thinking concepts and social determinants of health approaches are present in the design processes of three federal and three provincial strategies in Canada. Each case applies an original analytical framework to policy documents and interviews with senior strategy design leaders (n=18). Through comparative analysis, I find that that although no cases formally applied systems thinking to strategy design, the concepts are present to a significant, moderate, or limited extent. The analysis also presents a set of contextual factors in the policy environment that help or hinder the use of systems thinking. Contextual factors include the evidence-base of the strategy topic and theory of change, the policy environment and policy levers available, the specifics of the strategy design process and operational resources, and the leadership, culture, and skills of the workforce. This study contributes to the calls for evidence of applied systems thinking in public health policy and systems research.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".