Addressing Complexity in Chronic Disease Prevention Research
Bibliographic record
Abstract
There is wide agreement on the need for systems thinking to address complexity in chronic disease prevention but there is insufficient understanding of how such approaches are operationalised in prevention research. Ison and Straw propose that to address complexity, the right balance must be struck between ‘systemic’ and ‘systematic’ paradigms. We examined the nature and characteristics of this relationship in a series of six qualitative case studies of prevention research. Data comprised 29 semi-structured interviews with 16 participants, and online documents. The analysis combined inductive methods from grounded theory with a theoretically informed framework analysis. Systemic and systematic ways of working varied across each case as a whole, and within the dimensions of each case. Further, the interplay of systemic and systematic approaches was described along a dynamic continuum of variable proportions, with greater emphasis on systemic aspects balanced by less focus on the systematic, and vice versa. By expanding the boundaries for exploring prevention research, we gained empirical understanding of the potential and scope of systemic and systematic paradigms for addressing complexity in prevention research. There is inherent value in being more explicitly conscious and bilingual in both systemic and systematic paradigms so that their respective value and strengths may be utilised. Our findings propose a coherent theoretical frame to better understand existing approaches for addressing complexity in prevention research.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| grok | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| opus | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.169 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.016 | 0.070 |
| Scholarly communication | 0.023 | 0.035 |
| Open science | 0.004 | 0.030 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".