Vulnerability and Stressors for Burnout Within a Population of Hospital Nurses: A Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: The multitude of negative consequences of nurse burnout calls for interventions to protect the well-being of the individual nurses, patients, and hospital organizations. However, much is still to be discovered about the development of this complex psychological syndrome. PURPOSE: This study aimed to describe the development of nurse burnout for a population of Flemish hospital nurses while considering vulnerability and situational stressors as indicated by the vulnerability-stress model. METHODS: Ten registered nurses were enlisted for semistructured interviews through purposive sampling. All selected nurses were currently suffering from burnout, showed a burnout risk, or had gone through a burnout in the past. A descriptive thematic analysis was performed with themes inductively emerging from the data. RESULTS: Four main themes emerged: "being passionate about doing well or being good," "teamwork," "manager," and "work and personal circumstances." More specifically, it was the discrepancy between the first individual vulnerability factor and the three situational stressors that led to feelings of stress and burnout. CONCLUSIONS: The essence of the development of nurse burnout was found in the discrepancy between individual vulnerability and situational stressors. Therefore, we recommend burnout prevention to target both factors.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".