An integrative review of burnout and related concepts in nursing faculty
Bibliographic record
Abstract
Introduction: It is essential to support the health and well-being of nursing faculty. Nurse well-being is imperative for promoting many outcomes in health care and education. In the presence of workplace stressors, nursing faculty may experience negative impacts, including burnout. This integrative review explored the literature on burnout and related concepts in nursing faculty.Methods: An integrative review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram was performed. Articles were identified from databases, including PubMed and CINAHL Plus, citation searching, and content expert referral. Key search terms included “nursing faculty”, “burnout”, and “nursing education”. 102 articles were identified and screened for established inclusion criteria and 23 were included in this review.Results: A total of 23 articles exploring burnout and other related concepts in nursing faculty were appraised. Emergent themes encompassing contributing factors, manifestations, impact, and strategies for decreasing faculty burnout and increasing faculty well-being are illustrated in this review. Although a variety of individual and organizational strategies for decreasing burnout were emphasized in the literature, multiple gaps were identified. These gaps include 1) lack of comprehensive programs to address faculty burnout, 2) integration of skills and practices into nursing education curricula, 3) impact of interventions on educational outcomes, 4) assessments examining faculty needs, and 5) absence of best practices replicated in nursing education.Conclusions: It is imperative to explore a comprehensive approach to decreasing burnout and supporting faculty and student well-being in nursing education and examine methodological challenges in defining related concepts and measures.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".