Translation of Evidence-Based Interventions Into Oncology Care Settings
Bibliographic record
Abstract
BACKGROUND: Adoption of evidence remains slow, leading to variations in practices and quality of care. Examining evidence-based interventions implemented within oncology settings can guide knowledge translation efforts. OBJECTIVE: This integrative review aimed to (1) identify topics implemented for oncology-related evidence-based practice (EBP) change; (2) describe frameworks, guidelines, and implementation strategies used to guide change; and (3) evaluate project quality. METHODS: PubMed and CINAHL were searched to identify published practice change projects. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines were followed. Fifty articles met the inclusion criteria. Data were extracted; content analysis was conducted. The Quality Improvement Minimum Quality Criteria Set guided quality assessment. RESULTS: Topics included infection control/prevention (n = 18), pain/palliative care (n = 13), psychosocial assessment (n = 11), and medication adherence (n = 8). Among the projects, Plan, Do, Study, Act (n = 8) and Lean Six Sigma (n = 6) frameworks were used most. Thirty-six projects identified guidelines that directed interventions. Multiple implementation strategies were reported in all articles with planning, education, and restructuring the most common. Reach, sustainability, and ability to be replicated were identified as quality gaps across projects. CONCLUSION: The EBP topics that emerged are consistent with the oncology nursing priorities, including facilitating integration of EBP into practice. The studies identified used national guidelines and implementation strategies to move evidence into practice. Heterogeneity in measurement made synthesis of findings difficult across studies, although individual studies showed improvement in patient outcomes. IMPLICATIONS FOR PRACTICE: Development of an interprofessional oncology consortium could facilitate a standardized approach to implementation of high-priority topics that target improved patient outcomes, harmonize measures, and accelerate translation of evidence into practice.
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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 | 0.111 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".