Mobilising knowledge in complex health systems: a call to action
Bibliographic record
Abstract
Worldwide, policymakers, health system managers, practitioners and researchers struggle to use evidence to improve policy and practice. There is growing recognition that this challenge relates to the complex systems in which we work. The corresponding increase in complexity-related discourse remains primarily at a theoretical level. This paper moves the discussion to a practical level, proposing actions that can be taken to implement evidence successfully in complex systems. Key to success is working with, rather than trying to simplify or control, complexity. The integrated actions relate to co-producing knowledge, establishing shared goals and measures, enabling leadership, ensuring adequate resourcing, contributing to the science of knowledge-to-action, and communicating strategically.
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
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 | 0.240 | 0.181 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.018 | 0.144 |
| Scholarly communication | 0.048 | 0.087 |
| Open science | 0.014 | 0.050 |
| Research integrity | 0.065 | 0.056 |
| 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".