A Whole Systems Research Approach to Cancer Care: Why Do We Need It and How Do We Get Started?
Bibliographic record
Abstract
Because cancer care is presently developing into a complicated network of interventions delivered at different times and places with different intentions, there is a need to consider whether the current research approaches in clinical cancer care adequately cover the ongoing treatment choices and combinations. Researchers in complementary and alternative medicine (CAM) are proposing whole systems research as an additional research approach for modern systems of care, whether they include complementary and alternative medicine or not. The current status of whole systems research methodology development is mainly theoretical. Necessary components of the methodology include focus on interventions, context, process, outcomes, and philosophy. Further development should be based on observational studies using both qualitative and quantitative approaches, often combined. Only when modern healthseeking systems of treatment behaviors are thoroughly understood should fine-tuning of hypothesis-testing research methods be continued.
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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 | 0.119 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.020 | 0.037 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".