Evaluating Complex Healthcare Systems: A Critique of Four Approaches
Bibliographic record
Abstract
The purpose of this paper is to bring clarity to the emerging conceptual and methodological literature that focuses on understanding and evaluating complex or 'whole' systems of healthcare. An international working group reviewed literature from interdisciplinary or interprofessional groups describing approaches to the evaluation of complex systems of healthcare. The following four key approaches were identified: a framework from the MRC (UK), whole systems research, whole medical systems research described by NCCAM (USA) and a model from NAFKAM (Norway). Main areas of congruence include acknowledgment of the inherent complexity of many healthcare interventions and the need to find new ways to evaluate these; the need to describe and understand the components of complex interventions in context (as they are actually practiced); the necessity of using mixed methods including randomized clinical trials (RCTs) (explanatory and pragmatic) and qualitative approaches; the perceived benefits of a multidisciplinary team approach to research; and the understanding that methodological developments in this field can be applied to both complementary and alternative medicine (CAM) as well as conventional therapies. In contrast, the approaches differ in the following ways: terminology used, the extent to which the approach attempts to be applicable to both CAM and conventional medical interventions; the prioritization of research questions (in order of what should be done first) especially with respect to how the 'definitive' RCT fits into the process of assessing complex healthcare systems; and the need for a staged approach. There appears to be a growing international understanding of the need for a new perspective on assessing complex healthcare systems.
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 | 0.417 | 0.413 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.006 | 0.091 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.013 | 0.023 |
| Research integrity | 0.010 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".