The relationship between burnout and job satisfaction among psychiatric nurses
Bibliographic record
Abstract
Objective: One of the core concerns in psychiatric nursing is job burnout among nurses, because burnout had harmful impacts on both nurses’ health and their ability to cope with job demands. Moreover, long term job stress can cause burn out and reduce their level of satisfaction. Aim: The aim of the study was to explore the relationship between burnout aand job satisfactions among psychiatric nurses.Methods: Descriptive correlation design was utilized. The study was conducted at Psychiatric Department in Tanta University Hospital and Tanta Mental Health Hospital. The study sample consisted of 50 staff nurses. Tools were utilized for collection of data: First, the Burnout Inventory by Maslach; Second, the Job Satisfaction scale. It measured the general job satisfaction of the nursing staff. This scale has five domains: Personal factors, Work organization, Content and amount of work, Working unit and Leadership.Results: It was found that the majority of nurses had job dissatisfaction. In relation to staff nurses’ burnout, staff nurses’ job burnout and its components were found. It was observed that the majority of nurses had high burnout. Regarding burnout components, specifically, in relation to emotional exhaustion, it was found that the majority of nurses experienced high emotional exhaustion and depersonalization compared low accomplishment. It was found that there was significant negative correlation between burnout and job satisfaction, the highest frequency of nurses had high burnout and had low level of job satisfaction.Conclusions: The highest frequency of nurses had high burnout and had low level of job satisfaction. It recommended newly developed interventions to alleviate nurses’ burnout and increase job satisfaction, thereby enhancing the quality of healthcare. So, further support of managers in the prevention of burnout is a necessity. Thus, it will enhance creativity, job satisfaction, self-worthiness, and service quality.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".