Perceptions of Quality, Safety, and Harm in Oncology Nursing Practice
Bibliographic record
Abstract
BACKGROUND: Oncology care is complex, increasing the risk of patient harm. Nurses play a key role in identifying and addressing safety issues. Gaps in nurses' understanding of quality, safety, and harm may impede improvement efforts, particularly in safety reporting. PURPOSE: We examined oncology nurses' experiences with and perceptions of quality and safety in patient care, including their understanding of harm and how they use safety reporting systems in clinical practice. METHODS: We used interpretive description methodology and conducted semi-structured interviews with 28 nurses at an urban oncology center. RESULTS: Oncology nurses' accounts of quality, safety, and harm were nuanced and closely connected. Safety reporting systems present challenges and limitations in capturing nurses' concerns. CONCLUSION: Expanding harm definitions, streamlining reporting, and ensuring meaningful organizational responses are essential for fostering a culture of safety and quality improvement in oncology.
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How this classification was reachedexpand
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".