An Evolutionary Concept Analysis of Bullying Towards Nursing Students in the Clinical Practice Environment
Bibliographic record
Abstract
Concept analysis is a technique used to determine defining attributes, with the purpose of developing a concept to expand nursing knowledge. The interrogation of attributes, antecedents, and consequences aids in clarifying ambiguity with a view to developing an operational definition. Establishing a clear definition of a concept lays the groundwork for future research and practice. Nursing students embark on a sequenced set of clinical learning experiences as a primary component of their undergraduate program. Being bullied is common in clinical practicum experiences for nursing students and is often seen as unavoidable, resulting in negative impacts on student learning, health care team functioning deterioration, decreased quality of care for patients, and negative impacts on retention and satisfaction with the nursing profession. Numerous studies have investigated the concept of bullying in the profession, yet the range of how bullying manifests creates a challenge for analysis and agreement of the concept. Rodgers’s (2000) evolutionary method was used for this literature-based concept analysis to lead to a meaningful understanding of the concept over time by identifying attributes, antecedents, and consequences. Attributes specific to bullying of nursing students in the clinical environment included intentionality, temporality, negative interactions, denied opportunities, and workload challenges. Several surrogate terms were identified in this analysis. Antecedents identified for bullying included power imbalances, working conditions, entrenched behaviour, and organizational dysfunction. Consequences associated with bullying affect students, the nursing profession, and the workplace. The most prevalent finding from this concept analysis is that bullying is used to describe many different types of behaviour. This analysis contributes to the foundation of evidence supporting the discussion around the need for continued clarity and further research on the phenomenon of bullying in the clinical environment. Further analysis is needed to clarify the concept of bullying to expand knowledge in the nursing discipline by addressing the antecedents to influence change in nursing education. Clarity on how bullying is defined is necessary to facilitate recognizing and reporting bullying and developing effective interventions.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".