Applying systems thinking to knowledge mobilisation in public health
Bibliographic record
Abstract
CONTEXT: Knowledge mobilisation (KM) is a vital strategy in efforts to improve public health policy and practice. Linear models describing knowledge transfer and translation have moved towards multi-directional and complexity-attuned approaches where knowledge is produced and becomes meaningful through social processes. There are calls for systems approaches to KM but little guidance on how this can be operationalised. This paper describes the contribution that systems thinking can make to KM and provides guidance about how to put it into action. METHODS: We apply a model of systems thinking (which focuses on leveraging change in complex systems) to eight KM practices empirically identified by others. We describe how these models interact and draw out some key learnings for applying systems thinking practically to KM in public health policy and practice. Examples of empirical studies, tools and targeted strategies are provided. FINDINGS: Systems thinking can enhance and fundamentally transform KM. It upholds a pluralistic view of knowledge as informed by multiple parts of the system and reconstituted through use. Mobilisation is conceived as a situated, non-prescriptive and potentially destabilising practice, no longer conceptualised as a discrete piece of work within wider efforts to strengthen public health but as integral to and in continual dialogue with those efforts. A systems approach to KM relies on contextual understanding, collaborative practices, addressing power imbalances and adaptive learning that responds to changing interactions between mobilisation activities and context. CONCLUSION: Systems thinking offers valuable perspectives, tools and strategies to better understand complex problems in their settings and for strengthening KM practice. We make four suggestions for further developing empirical evidence and debate about how systems thinking can enhance our capacity to mobilise knowledge for solving complex problems - (1) be specific about what is meant by 'systems thinking', (2) describe counterfactual KM scenarios so the added value of systems thinking is clearer, (3) widen conceptualisations of impact when evaluating KM, and (4) use methods that can track how and where knowledge is mobilised in complex systems.
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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 |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Scholarly communication Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.036 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".