Strategies facilitating practice change in pediatric cancer: a systematic review
Bibliographic record
Abstract
PURPOSE: By conducting a systematic review, we describe strategies to actively disseminate knowledge or facilitate practice change among healthcare providers caring for children with cancer and we evaluate the effectiveness of these strategies. DATA SOURCES: We searched Ovid Medline, EMBASE and PsychINFO. STUDY SELECTION: Fully published primary studies were included if they evaluated one or more professional intervention strategies to actively disseminate knowledge or facilitate practice change in pediatric cancer or hematopoietic stem cell transplantation. DATA EXTRACTION: Data extracted included study characteristics and strategies evaluated. In studies with a quantitative analysis of patient outcomes, the relationship between study-level characteristics and statistically significant primary analyses was evaluated. RESULTS OF DATA SYNTHESIS: Of 20 644 titles and abstracts screened, 146 studies were retrieved in full and 60 were included. In 20 studies, quantitative evaluation of patient outcomes was examined and a primary outcome was stated. Eighteen studies were 'before and after' design; there were no randomized studies. All studies were at risk for bias. Interrupted time series was never the primary analytic approach. No specific strategy type was successful at improving patient outcomes. CONCLUSIONS: Literature describing strategies to facilitate practice change in pediatric cancer is emerging. However, major methodological limitations exist. Studies with robust designs are required to identify effective strategies to effect practice change.
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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 | 0.021 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".