Innovation in Health Systems and Organizations
Bibliographic record
Abstract
Abstract Pressure to improve and transform has led to cycles of reform and policy change in health systems, accompanied by calls for innovation to address persistent vulnerabilities. This chapter explores the dynamics of innovation within health systems and identifies new lines of inquiry for researchers and insights for practitioners. Our analysis is based on the exploration of four research areas anchored in different research and epistemological traditions for the study of innovation. Implementation science is concerned with evidence-informed innovations to support practice change in health systems. Normalization process theory focuses on the work performed by adopters and implementors to adapt innovations to context. Translation theories analyse the dynamics of spreading innovations across organizations and networks. Disruptive innovation identifies attributes of innovation that challenge and transform existing practices. Our analysis suggests three lines of inquiry to advance research on innovations. First, more attention should be paid to context as a multilayered, proximal, and distal phenomenon. Second, recognition of the importance of actors’ agentic capacities and how their practices both shape and are shaped by innovations appears crucial. Third, we need to better understand the processes that make it difficult to determine the effectiveness and impact of innovations. This perspective for the study of innovation has practical implications for the development of leadership in organizations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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, 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".