A qualitative evaluation from a complex systems perspective of a whole systems approach to obesity in an English local authority
Bibliographic record
Abstract
Aims: Current thinking suggests that whole systems approaches (WSAs) may be required to tackle obesity. WSAs are policy approaches designed to address problems in the context of the complex system in which they occur. Evaluating a WSA is a key challenge and there is a significant lack of knowledge on this topic. The aim of this study was to carry out an evaluation, from a complex systems perspective, of the impact of a WSA to obesity implemented in the English county of East Sussex. This study addresses gaps in knowledge regarding methodological approaches to evaluating WSAs in general, and specifically the use of qualitative methods to evaluate system change. Methods: Semi-structured interviews were conducted with 17 professionals in East Sussex County Council and eight partner organisations. Analysis was guided by a complex systems evaluation framework developed by McGill et al., with analytical output presented as a narrative of the system undergoing change. In an original addition, a local obesity system map was used to depict the location of changes within the system. Results: Three key changes, all located in the ‘Workforce development’ area of the system map, were identified following implementation of the WSA: (a) it significantly influenced the creation of a new post at the County Council to address environmental determinants of obesity, (b) it increased knowledge of the role of wider determinants of obesity and (c) it improved partnership working. Conclusions: This study illustrates how a WSA to obesity can be evaluated from a complex systems perspective using qualitative methods and offers practical insights for designing future evaluations of WSAs, particularly in relation to data analysis and the use of system mapping.
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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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".