Bibliographic record
Abstract
Workplace violence is an area of increasing concern worldwide. Issues of violence are well documented in nursing. To address this, a better understanding of the culture of nursing academia is required. Problems of incivility are reported between students, between students and faculty, and between faculty and faculty. The purpose of this study is to increase understanding of faculty to faculty violence in nursing academia. Guided by a theoretical framework incorporating the perspectives of Mason and Foucault and specifically on the concepts of violence, power, knowledge, difference and resistance, this study focuses on aspects of the social and cultural work environment, and organizational policies and procedures influencing workplace violence between faculty members. Using principles from critical ethnography, the research was conducted within three schools of nursing at universities in eastern Canada. Data collection included 29 semi-structured interviews with nursing faculty, key informants (including representation from management, human resources, support staff and human rights office) and mute document review. Three major themes emerged: the academic apparatus, experiencing academia, and coping mechanisms. Nursing academic culture is divergent, exhibiting fierce competitiveness and elitism, intertwined with pockets of support and resilience. Faculty identified diverse personal and professional strategies employed to withstand the challenges. Need for change was expressed by some faculty and managers. These findings may inform the efforts of faculty and management seeking transformation to a less competitive and elitist culture.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".