A nursing perspective on the antecedents and consequences of incivility in higher education: A scoping review
Bibliographic record
Abstract
Background: Understanding antecedents and consequences of incivility across higher education is necessary to create and implement strategies that prevent and slow uncivil behaviors. Purpose: To identify the nature, extent, and range of research related to antecedents and consequences of incivility in higher education. Objectives: 1) To identify disciplines and programs sampled in higher education incivility research, and 2) to compare antecedents and consequences examined in nursing education research with other disciplines and programs in higher education. Design: A scoping review of the literature. Data sources: Eight electronic databases searched in January 2023 including MEDLINE Ovid, CINAHL, ERIC, PsycINFO, Scopus, ProQuest Education Database, Education Research Complete, and ProQuest Dissertations and Theses Global. Review methods: We included primary research articles examining antecedents or consequences of incivility in higher education. Two reviewers independently screened and determined inclusion of each study. Data extraction was completed. We employed a numerical descriptive summary to analyze the range of data and content analysis to categorize the antecedents and consequences of incivility in higher education. Results: = 2 other programs) were reported as consequences of incivility in higher education. Conclusions: Supporting development of teaching practices and role modeling of civility by faculty is a crucial element to slowing the frequency of uncivil interactions between faculty and students. Specific strategies that target stress, such as, cognitive behavioral therapy, coping skills, and social support could mitigate incivility in higher education. Future research needs to examine the strength of the negative effects of incivility on physiological and psychological outcomes through advanced statistical methods, as well as the cumulative effects of uncivil behavior on these outcomes over time for both students and faculty. Application of advanced statistical methods can also support our understanding of sources of incivility as well as the accuracy of causal connections between its antecedents and consequences.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".