Clinical judgment during the nursing process in oncology care: An almost experimental study
Bibliographic record
Abstract
Background: Nurses face difficulties in applying the stages of the nursing process, which may lead to shortcomings in clinical judgment or decision-making in patient care. Objective: To compare the clinical judgment of nurses providing care to people with cancer, before and after an educational intervention regarding use of nursing process. Methods: This is a quasi-experimental study carried out with 12 nurses working in a high-complexity oncology hospital in Brazil. The participants were assessed using the Lasater Clinical Judgment Rubric (LCJR) before and after an educational intervention on nursing process. Data collection occurred between July and August 2023, and the data were analyzed using Student’s t-test for paired samples. Results: The study variables that showed significance after intervention were prioritizing data (p < 0.001), making sense of data pre-intervention (p = 0.017), well-planned intervention/flexibility (p = 0.002), being skillful (p = 0.003), evaluation/self-analysis (p < 0.001), and commitment to improvement (p < 0.001). Conclusions: It can be seen through this study that oncology nurses improved their interpretation skills, their response to the provision of nursing care, and their ability to reflect on their practice following an educational intervention. The education intervention is an element that changes realities, such that nurses are competent to implement the nursing process in cancer care settings.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".