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Record W2991162881 · doi:10.5430/jnep.v10n3p8

The relationship between burnout and job satisfaction among psychiatric nurses

2019· article· en· W2991162881 on OpenAlexvenueno aff
Sahar Behilak, Ayat Saif-elyazal Abdelraof

Bibliographic record

VenueJournal of Nursing Education and Practice · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutDepersonalizationJob satisfactionEmotional exhaustionNursingPsychologyScale (ratio)MedicineClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.130
GPT teacher head0.525
Teacher spread0.395 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations11
Published2019
Admission routes1
Has abstractyes

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