Overcoming Barriers to Applying Systems Thinking Mental Models in Policy-Making 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
Systems thinking provides the health system with important theories, models and approaches to understanding and assessing complexity. However, the utility and application of systems thinking for solution-generation and decision-making is uncertain at best, particularly amongst health policy-makers. This commentary aims to elaborate on key themes discussed by Haynes and colleagues in their study exploring policy-makers' perceptions of an Australian researcher-policy-maker partnership focused on applications of systems thinking. Findings suggest that policy-makers perceive systems thinking as too theoretical and not actionable, and that the value of systems thinking can be gleaned from greater involvement of policy-makers in research (ie, through co-production). This commentary focuses on the idea that systems thinking is a mental model that, contrary to researchers' beliefs, may be closely aligned with policy-makers' existing worldviews, which can enhance adoption of this mental model. However, wider application of systems thinking beyond research requires addressing multiple barriers faced by policy-makers related to their capability, opportunity and motivation to action their systems thinking mental models. To make systems thinking applicable to the policy sphere, multiple approaches are required that focus on capacity building, and a shift in shared mental models (or the ideas and institutions that govern policy-making).
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 | 0.046 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.021 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.073 | 0.074 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".