Responding to disruptive behaviors in nursing: One-year follow-up of quasi-experimental research measuring links to turnover, intent to leave, and patient care quality
Bibliographic record
Abstract
Background and objectives: Disruptive behaviors among nurses are a prevalent problem in health care, contributing to nursing staff turnover and compromising patient care. Newly licensed nurses may be unprepared to respond to disruptive behaviors effectively, negatively impacting them, patients, and organizations. Cognitive rehearsal can increase self-efficacy to respond effectively to disruptive behaviors. The purpose of this study was to determine the longitudinal impact of a cognitive rehearsal intervention delivered to nursing students during the final semester of their pre-licensure program on self-efficacy to respond to disruptive behaviors, turnover and intent to stay in a job, frequency of disruptive behaviors, and perceived impact on patient care.Methods: Design: This study was the second phase in a quasi-experimental, longitudinal project. Participants and Setting: In Phase 1, 129 participants were recruited from three pre-licensure nursing programs in the Midwestern United States. All participants received the intervention. In Phase 2, one year after graduating, 95 remained enrolled. Methods: An electronic survey was used to collect data. Paired t-tests were used to detect changes in self-efficacy; bi-variate correlations were utilized to determine relationships between outcome variables.Results: Multiple measures of self-efficacy to respond remained statistically significantly increased one year after graduating (p < .05). Experiencing (r = .489; p < .000) and witnessing (r = .432; p < .000) disruptive behaviors was significantly linked to patient care.Conclusions: Cognitive rehearsal had a sustained, positive impact on self-efficacy to respond to disruptive behaviors and should be included in pre-licensure curricula.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".