The landscape of knowledge translation interventions in cancer control: What do we know and where to next? A review of systematic reviews
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
BACKGROUND: Effective implementation strategies are needed to optimize advancements in the fields of cancer diagnosis, treatment, survivorship, and end-of-life care. We conducted a review of systematic reviews to better understand the evidentiary base of implementation strategies in cancer control. METHODS: Using three databases, we conducted a search and identified English-language systematic reviews published between 2005 and 2010 that targeted consumer, professional, organizational, regulatory, or financial interventions, tested exclusively or partially in a cancer context (primary focus); generic or non-cancer-specific reviews were also considered. Data were extracted, appraised, and analyzed by members of the research team, and research ideas to advance the field were proposed. RESULTS: Thirty-four systematic reviews providing 41 summaries of evidence on 19 unique interventions comprised the evidence base. AMSTAR quality ratings ranged between 2 and 10. Team members rated most of the interventions as promising and in need of further research, and 64 research ideas were identified. CONCLUSIONS: While many interventions show promise of effectiveness in the cancer-control context, few reviews were able to conclude definitively in favor of or against a specific intervention. We discuss the complexity of implementation research and offer suggestions to advance the science in this area.
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 | 0.024 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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