Bibliographic record
Abstract
Complexity is a form of system that is relevant in virtually all aspects and levels of healthcare. The importance and relevance of the science of complexity for healthcare has been written about extensively for over two decades. The problem this article addresses is that complex systems and how to manage them still appear to be relatively unknown nor well understood by most stakeholders of healthcare. The ignorance of complexity science includes multiple dimensions of healthcare, including frontline practitioners, support staff, and healthcare administrators. An additional challenge is to also involve policy-makers, and indeed the general public in advancing their appreciation of how this still evolving field of study can improve healthcare efficiency and outcomes. A third challenge is acceptance and willingness to embrace complexity. This article discusses a framework for evaluating complexity acceptance, as well as the unique challenges that the existing healthcare infrastructure and culture exhibit that are hindering adoption of complexity. Suggestions are put forward for further exploration and consideration.
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 | 0.088 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".