Hospital Nurses in Comparison to Community Nurses: Motivation, Empathy, and the Mediating Role of Burnout
Bibliographic record
Abstract
BACKGROUND: Hospital nurses' experience of their profession differs from that of community clinic nurses due to different working conditions and settings. PURPOSE: To compare hospital nurses and community clinic nurses as to the mediating role of burnout on motivation and empathy. METHODS: In this cross-sectional study, 457 nurses completed four questionnaires: Demographic, Motivation Questionnaire, the Maslach Burnout Inventory, and the Toronto Empathy Questionnaire. RESULTS: Emotional exhaustion and depersonalization among hospital nurses were significantly higher than among community nurses. No significant differences were found in personal accomplishment, empathy, and motivation between the groups. Empathy and motivation were more strongly correlated among hospital nurses than among community nurses. Burnout was found to be a significant mediator between empathy and motivation in both groups but in each group by different burnout subscales. CONCLUSIONS: To reduce burnout, leaders in the nursing field must enhance conditions in the hospital nurses' work environment to lower levels of emotional exhaustion and depersonalization; community nurses should be guided to improve their attitudes toward their on-the-job performance to promote their personal accomplishment. Understanding the differences could direct policy makers' desire toward enacting policies that accommodate these differences and focus on the needs of both groups of professionals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".