W(h)ither complexity? The emperor's new toolkit? Or elucidating the evolution of health systems knowledge?
Bibliographic record
Abstract
RATIONALE: The outputs from vastly expanding health research and knowledge industry with a broadening range of approaches to the synthesis of knowledge provide an impetus to develop complex science and theory-informed knowledge management in health care. Aims To stimulate debate in order to assist health care decision makers to move beyond framing certainty and evidence in purely reductionist terms. OBJECTIVES: To locate health, health care and health knowledge systems research using a complex adaptive systems theory framework. Methods An conceptual analysis of pervading methodologies and ways of knowing in health systems research to elucidate a framework in order to inform health care decision making. FINDINGS: A living Tree of (Research) Knowledge is proposed, with theoretic and operational frameworks. Branches of the tree are linked to differing evolutionary and developmental processes in order to assist researchers in the ongoing self-organizing of taxonomies, multiple methods and types of knowledge, recognizing the 'lived', developing and adaptive nature of our understandings. CONCLUSIONS: It is challenging to determine whither the directions 'knowledge' creation and management should take in complex health systems, beyond a total reliance on reductionism. Yet quality will wither, if knowledge does not pertain to real world contexts.
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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.074 |
| Scholarly communication | 0.015 | 0.043 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".