Situational and dispositional predictors of nurse manager burnout: a time-lagged analysis
Bibliographic record
Abstract
BACKGROUND: Burnout among nurses is a serious condition that threatens their own health and that of their patients. In current health care settings, nurses are particularly at risk for burnout given the increased patient acuity and the worsening nursing shortage. AIM: This study examined the influence of effort-reward imbalance, a situational variable, and core self-evaluation, a dispositional variable, on nurse managers' burnout levels over a 1-year period. METHODS: A predictive longitudinal survey design was used to examine the relationships described in the model. One hundred and thirty-four nurse managers responded to a mail survey at two points in time. RESULTS: As hypothesized, both personal and situational factors influenced nurse manager burnout over a 1-year time frame. Although burnout levels at Time 1 accounted for significant variance in emotional exhaustion levels 1 year later (beta = 0.355), nurses' effort-reward imbalance (beta = 0.371) and core self-evaluations (beta = -0.166) explained significant additional amounts of variance in burnout 1 year later. CONCLUSION: Both personal and situational factors contribute to nurse manager burnout over time. Implications for nursing management Managers must consider personal and contextual factors when creating work environments that prevent burnout and foster positive health among nurses at work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".