Job Satisfaction and Associated Factors Among Health Care Professionals Working in Public Health Facilities in Ethiopia: A Systematic Review
Bibliographic record
Abstract
Hailemichael Kindie Abate, Chilot Kassa Mekonnen Medical Nursing Department, College of Medicine and Health Science, University of Gondar, Gondar, EthiopiaCorrespondence: Hailemichael Kindie Abate Lidita 16, P.o.box, 196, Gondar, EthiopiaEmail haile206k@gmail.comBackground: Job satisfaction is a feeling that measure cognitive and behavioral aspects of workers’ towards their job. According to the World Health Organization report, it predicts that 40% of health care professionals’ (nurses, midwives, and doctors) will leave their job as a result of job dissatisfaction.Methods: Studies were searched systematically using International databases from PubMed, Google Scholar, Cochrane Library, Embase, and CINAHL. The quality of searched articles assessed using the New Castle Ottawa scale for a cross-sectional study design. Statistical analysis was performed by using STATA version 14 software for window and systemic review carried out using a random effect method. The Preferred Reporting Item for Systematic Review and Meta-Analyses (PRISMA) guideline was followed for reporting results.Results: From the total 1120 records screened, 8 studies with 4092 participants who fulfilled the inclusion criteria were included in this systematic review. The estimated pooled prevalence of job satisfaction of health care professionals in Ethiopia was 41.17%.Conclusion: About one in three health care professionals were satisfied. Therefore, the government and health institution should focus on strategies to promote health care professionals’ of job satisfaction.Keywords: job satisfaction, health care professional, health institutions, Ethiopia
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".