Interventions to Promote Safety Culture in Cancer Care: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: There is limited guidance on how to effectively promote safety culture in health care settings. We performed a systematic review to identify interventions to promote safety culture, specifically in oncology settings. METHODS: Medical Subject Headings and text words for "safety culture" and "cancer care" were combined to conduct structured searches of MEDLINE, EMBASE, CDSR, CINAHL, Cochrane CENTRAL, PsycINFO, Scopus, and Web of Science for peer-reviewed articles published from 1999 to 2021. To be included, articles had to evaluate a safety culture intervention in an oncology setting using a randomized or nonrandomized, pre-post (controlled or uncontrolled), interrupted time series, or repeated-measures study design. The review followed PRISMA guidelines; quality of included citations was assessed using the ROBINS-I risk of bias tool. RESULTS: Eighteen articles meeting the inclusion criteria were retained, reporting on interventions in radiation (14 of 18), medical (3 of 18), or general oncology (1 of 18) settings. Articles most commonly addressed incident learning systems (7 of 18), lean initiatives (4 of 18), or quality improvement programs (3 of 18). Although 72% of studies reported improvement in safety culture, there was substantial heterogeneity in the evaluation approach; rates of reporting of adverse events (9 of 18) or Agency for Healthcare Research and Quality Safety Culture survey results (9 of 18) were the most commonly used metrics. Most of the studies had moderate (28%) or severe (67%) risk of bias. CONCLUSIONS: Despite a growing evidence base describing interventions to promote safety culture in cancer care, definitive recommendations were difficult to make because of heterogeneity in study designs and outcomes. Implementation of incident learning systems seems to hold most promise.
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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 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".