Designing health innovation networks using complexity science and systems thinking: the CoNEKTR model
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Complex problems require strategies to engage diverse perspectives in a focused, flexible manner, yet few options exist that fit with the current health care and public health system constraints. The Complex Network Electronic Knowledge Translation Research model (CoNEKTR) brings together complexity science, design thinking, social learning theories, systems thinking and eHealth technologies together to support a sustained engagement strategy for social innovation support and enhancing knowledge integration. METHODS: The CoNEKTR model adapts elements of other face-to-face social organizing methods and combines it with social media and electronic networking tools to create a strategy for idea generation, refinement and social action. Drawing on complexity science, a series of networking and dialogue-enhancing activities are employed to bring diverse groups together, facilitate dialogue and create networks of networks. RESULTS: Ten steps and five core processes informed by complexity science have been developed through this model. Concepts such as emergence, attractors and feedback play an important role in facilitating networking among participants in the model. CONCLUSIONS: Using a constrained, focused approach informed by complexity science and using information technology, the CoNEKTR model holds promise as a means to enhance system capacity for knowledge generation, learning and action while working within the limitations faced by busy health professionals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.337 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".