How Openness Serves Innovation in Healthcare? Comment on "What Managers Find Important for Implementation of Innovations in the Healthcare Sector – Practice Through Six Management Perspectives"
Bibliographic record
Abstract
The recent study of which enabling factors can facilitate the specific step of moving from idea generation to implementation in healthcare supports that managing innovation is a context-driven process that goes through six categories of change. While this research provides a general and rather comprehensives overview of what successful innovation work needs, it does not offer deeper insights into how categories of change can be operated in the context of accelerated openness in healthcare. I use the concepts of open innovation and open strategy to trying better understand how openness, in terms of greater inclusion and transparency, may or may not serve healthcare innovation through three theoretical questions: to whom, how and when to open up to foster innovation? Whilst diversity of knowledge, actors and systems are growing drivers of innovation, strategizing openness for more deliberate and impactful inclusion and transparency in healthcare management is key to coproducing better health.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.070 | 0.059 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".