Cross-Disciplinary Research in Cancer: An Opportunity to Narrow the Knowledge–Practice Gap
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Health services researchers have consistently identified a gap between what is identified as "best practice" and what actually happens in clinical care. Despite nearly two decades of a growing evidence-based practice movement, narrowing the knowledge-practice gap continues to be a slow, complex, and poorly understood process. Here, we contend that cross-disciplinary research is increasingly relevant and important to reducing that gap, particularly research that encompasses the notion of transdisciplinarity, wherein multiple academic disciplines and non-academic individuals and groups are integrated into the research process. The assimilation of diverse perspectives, research approaches, and types of knowledge is potentially effective in helping research teams tackle real-world patient care issues, create more practice-based evidence, and translate the results to clinical and community care settings. The goals of this paper are to present and discuss cross-disciplinary approaches to health research and to provide two examples of how engaging in such research may optimize the use of research in cancer care.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 it