System Action Learning: Reorientating Practice for System Change in Preventive Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is now widely accepted that many of the problems we face in public health are complex, from chronic disease to COVID-19. To grapple with such complexity, researchers have turned to both complexity science and systems thinking to better understand the problems and their context. Less work, however, has focused on the nature of complex solutions, or intervention design, when tackling complex problems. This paper explores the nature of system intervention design through case illustrations of system action learning from a large systems level chronic disease prevention study in Australia. The research team worked with community partners in the design and implementation of a process of system action learning designed to reflect on existing initiatives and to reorient practice towards responses informed by system level insights and action. We were able to observe and document changes in the mental models and actions of practitioners and in doing so shine a light on what may be possible once we turn our attention to the nature and practice of system interventions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it