Blending integrated knowledge translation with global health governance: an approach for advancing action on a wicked problem
Bibliographic record
Abstract
BACKGROUND: The persistence of health inequities is a wicked problem for which there is strong evidence of causal roots in the maldistribution of power, resources and money within and between countries. Though the evidence is clear, the solutions are far from straightforward. Integrated knowledge translation (IKT) ought to be well suited for designing evidence-informed solutions, yet current frameworks are limited in their capacity to navigate complexity. Global health governance (GHG) also ought to be well suited to advance action, but a lack of accountability, inclusion and integration of evidence gives rise to politically driven action. Recognising a persistent struggle for meaningful action, we invite contemplation about how blending IKT with GHG could leverage the strengths of both processes to advance health equity. DISCUSSION: Action on root causes of health inequities implicates disruption of structures and systems that shape how society is organised. This infinitely complex work demands sophisticated examination of drivers and disrupters of inequities and a vast imagination for who (and what) should be engaged. Yet, underlying tendencies toward reductionism seem to drive superficial responses. Where IKT models lack consideration of issues of power and provide little direction for how to support cohesive efforts toward a common goal, recent calls from the field of GHG may provide insight into these issues. Additionally, though GHG is criticised for its lack of attention to using evidence, IKT offers approaches and strategies for collaborative processes of generating and refining knowledge. Contemplating the inclusion of governance in IKT requires re-examining roles, responsibilities, power and voice in processes of connecting knowledge with action. We argue for expanding IKT models to include GHG as a means of considering the complexity of issues and opening new possibilities for evidence-informed action on wicked problems. CONCLUSION: Integrated learning between these two fields, adopting principles of GHG alongside the strategies of IKT, is a promising opportunity to strengthen leadership for health equity action.
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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 | 0.168 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.011 | 0.087 |
| Scholarly communication | 0.032 | 0.043 |
| Open science | 0.007 | 0.050 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".