Addressing Workplace Bullying Among Undergraduate Nursing Students Using an Online Educational Tool
Bibliographic record
Abstract
Background: Workplace bullying among nurses is a prevalent and serious problem in health care with detrimental physical, psychological, and organizational consequences. Nursing students and novice nurses are more likely to encounter incidents of workplace bullying in their clinical settings. Although workplace bullying is one of the largest challenges that the nursing profession faces today, there is a scarcity of intervention research aimed at educating nursing students on effective responses to bullying. Purpose: To evaluate the effectiveness of an online educational tool in improving self-efficacy and intent to intervene related to bullying. Methods: The design was quasi-experimental, using a one-group pre-test/post-test approach. A convenience sampling technique was used. A total of 41 undergraduate nursing students from two baccalaureate nursing programs located in Western Canada completed a demographic questionnaire and the self-efficacy to respond to disruptive behaviours scale with an additional item about intent to intervene. A paired sample t-test was used for normally distributed data, and the Wilcoxon signed-rank test was used for data with non-normal distribution. Results: Both outcome measures (self-efficacy and intent to intervene) had significant pre- and post-intervention differences except for one measure of self-efficacy. Conclusion: This study adds to current nursing knowledge by developing and evaluating an evidence-based online educational tool to prepare nursing students in identifying and managing bullying in health 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".