A mixed methods analysis of predictors of a toxic culture among nurses
Bibliographic record
Abstract
Objective: The three-year Workforce Engagement for Compassionate Advocacy, Resiliency, and Empowerment (WE CARE) project targets well-being and resilience of post-pandemic nurses in a hospital environment as part of a 2020 grant-funded collaborative agreement with the Health Resources and Services Administration. This project aims to examine extrinsic factors associated with the perception of a toxic work environment among post-pandemic nursing personnel and further describe the toxicity present at work.Methods: A mixed methods design was conducted at an academic medical center in the Southeastern United States to assess nursing well-being. An open-ended question to explore nurses’ perceptions of toxic work environment was added to an annual email survey on well-being topics. All nursing personnel were solicited and 1,359 responded. Results: A total of 366 individuals (27%) selected toxic work environment as a stressor and 50 respondents commented contributing 218 instances of themes. Lack of leadership was the most frequent theme identified (63/218, 28.9%) but others included, in descending order, relational aggression, negative attitudes, lack of job accountability, gossip, favoritism, lack of teamwork, attitudes/bullying, negative work environment, cliques, and lack of trust. The respondents who perceived a toxic culture also reported significantly lower perceived organizational support (M = 7.22) than who did not (M = 9.21) (p < .001, Cohen’s D = 0.64); and other significantly worse outcomes including burnout (60.9% versus 33.5%, Cramer’s V = 0.22), and moral distress (34.4% versus 16.8%, Cramer’s V = 0.17).Conclusions: Although this was a single site study and cannot be generalized, the findings of 27% of nursing personnel experiencing a toxic work environment is notable. Perceived lack of leadership was the most prominent theme. Those reporting a toxic culture also reported lower indicators of well-being. This project should provide an impetus for others to investigate this phenomenon among their respective workforces.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".