Evidence-Based Decision-Making in Healthcare: Exploring the Issues through the Lens of Complex, Adaptive Systems Theory
Bibliographic record
Abstract
Browman, Snider and Ellis have articulated several reasons as to why and how managers should address the implementation of evidence-based decision-making (EBDM) in healthcare. While their observations are acknowledged to be from the unique perspective of an oncology setting, this is a timely and welcome lead article with significance in other settings. The authors invite opinions on transferability, thus forming the basis of this commentary. In response, this commentary offers a number of supportive and differing views. Complex, adaptive systems (CAS) theory is first addressed as an appropriate lens to reframe our conceptualization of the health system. Then, in contrast to negotiation, dialogue through participatory planning and decision-making is introduced. Evidence-based decision-making (EBDM) and knowledge translation (KT) are expanded upon in the context of CAS and participatory environments. Finally, concrete suggestions are offered on how to structure multiple-stakeholder involvement in the decision-making process, including the growing role of consumers in the new complex, adaptive systems reality of healthcare.
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 | 0.067 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.065 | 0.072 |
| Insufficient payload (model declined to judge) | 0.003 | 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".