The landscape of knowledge translation interventions in cancer control: What do we know and where to next? A review of systematic reviews
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.
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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 | 0.338 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.009 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| 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".