Nursing explanation skills in education and practice: Development skills and influence on incident occurrence
Bibliographic record
Abstract
Inaccurate explanations to patients, their families, and other healthcare professionals can adversely affect the quality of healthcare and patient safety. Despite the significance of good explanatory skills in nursing education and practice, supporting empirical data are limited. This study aimed to develop a psychological scale and investigate the impact of explanatory skills on patient safety by statistically testing the validity of hypothetical models derived from previous studies. In the preliminary investigation, 87 items were obtained from 109 experienced nurses. Study 1 involved an online explanatory skills survey with a sample of 1,000 nursing professionals. Study 2 comprised a field survey of 159 nursing staff working in a comprehensive hospital. Nine sub-skills, including seven common sub-skills and one specific sub-skill for each patient/family and staff, were identified and categorized under “compassion” and “shared mental model.” Clinical ladder progression was associated with both compassion and a shared mental model. Furthermore, compassion was identified as a factor that increased the probability of various incidents through interactional failures. Contrastingly, the shared mental model enhanced the probability of severe incidents due to judgmental and minor incidents from conceptual failures. This study developed a psychological scale to measure nursing explanation skills in communicating with patients, their families, and other medical staff and elucidated their impact on incident occurrence through miscommunication. Finally, the importance of accountability skills in nursing education and practice was discussed.
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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 | 0.003 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".