The Intermediary Role of Burnout in the Relationship Between Self-Compassion and Job Satisfaction Among Nurses
Bibliographic record
Abstract
BACKGROUND: Self-compassion is the process of treating oneself kindly especially when suffering. Self-compassion is associated with decreased symptoms of burnout in nurses such that those high in self-compassion are less likely to report emotional exhaustion, depersonalization, and a reduced sense of personal accomplishment. OBJECTIVES: Recruited from MTurk, a final sample of 158 nurses living in the United States (57.6% female) completed indices of self-compassion, burnout, and outcomes of satisfaction and sleep via an online survey. RESULTS: Self-compassion was positively associated with satisfaction and sleep quality and negatively associated with burnout. In addition, increased burnout was associated with lower satisfaction and poorer sleep quality. Mediational models suggested that the relationships between self-compassion and outcomes were accounted for by the shared variance between self-compassion and burnout. CONCLUSIONS: The results showed the predicted associations among self-compassion, burnout, and outcomes. Importantly, self-compassion was associated with lower burnout that, in turn, was associated with higher job satisfaction and better sleep. Despite the limitations of the study, these results add to the growing literature on the benefits of self-compassion for nurses for preventing and/or ameliorating symptoms of burnout.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".