Convergent Points for Conventional Medicine and Whole Systems Research: A User's Guide
Bibliographic record
Abstract
CONTEXT: Value-based health care has emerged as a manifestation of the conventional medicine community's awareness of the overlapping needs to both better incorporate patient centeredness into practice and research paradigms and further develop a systemic approach to cost reduction. BACKGROUND: The origins of the whole systems research (WSR) movement date to the late 1990s, when the U.S. Congress legislated appropriation of funds to stimulate the U.S. National Institutes of Health to evaluate popular traditional, complementary, and integrative medicine (TCIM) practices. Questions immediately arose over how well these forms of practice could be measured through standard randomized controlled trials, and the WSR community began to articulate and adapt innovative methodologies for evaluating TCIM interventions. DISCUSSION: This column explores the potential impact of WSR methods and exemplars on the clinical practice and research communities seeking to successfully implement and measure the complexities of value-based health care. Four potentially cross-talking themes are specifically discussed: complex behaviorally focused interventions, patient-centered outcomes, team-based care, and resilience and well-being. CONCLUSION: The time is ripe for clinicians and investigators to capitalize on methodologies, exemplars, and learnings from the WSR literature toward improving care, developing more robust research strategies, and furthering the dialogue between the TCIM and conventional medicine communities.
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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 | 0.071 | 0.194 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.028 | 0.029 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.011 | 0.021 |
| Insufficient payload (model declined to judge) | 0.057 | 0.038 |
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".