Do relational differences in demographics and work values result in conflict and burnout in the nursing workforce?
Bibliographic record
Abstract
The consequences of diversity have not been formally considered as contributing to undesirable work environments in healthcare. I sought to address this gap by examining a conceptual model that explains how diversity within the nursing workforce gives rise to interpersonal conflict (relationship and task) within workgroups, which in turn, is linked to burnout (emotional exhaustion, depersonalization, and diminished personal accomplishment). Diversity was defined as the degree of relative difference or dissimilarity between an individual and other workgroup members on select attributes, which in this study were age, education, ethnicity/race, and work values. Using a cross-sectional survey design, data were taken from a population-based sample of 603 nurses (registered nurses and licensed practical nurses) (80% response rate) in two acute care hospitals in British Columbia, Canada. At the individual level of analysis, a two-step approach to latent variable modelling was used: (a) factor analysis techniques to test and establish the validity of the measurement model and (b) structural equation modelling to test the hypothesized model. Partial support for the proposed model was found for both the direct relationships between diversity and burnout as well as the mediating effects of interpersonal conflict. Overall, the results indicated that perceived diversity explained a greater percentage of the variance in burnout compared with the explanatory power of actual diversity. Specifically, perceived work values and educational diversity were the most important explanatory variables of depersonalization (Pratt index = 58% and 21%, respectively) and were similarly predictive of diminished personal accomplishment (Pratt index = 69% and 35%, respectively). Emotional exhaustion was solely (Pratt index = 100%) explained by perceived work values diversity; however, the total variance explained was very minimal. Both individuals’ involvement in relationship and task conflict were the predominant mediating variables of the relationships between perceived work values diversity and emotional exhaustion (59% and 76% total mediation, respectively), depersonalization (57% and 68% total mediation, respectively), and diminished personal accomplishment (28% and 32% total mediation, respectively). The implications of the study relate to nurses and decision-makers at the micro, meso, and macro level of practice to create a climate of support for, and acceptance of, diversity in healthcare workplaces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".