Workplace Ethical Climate and Workers' Burnout: A Systematic Review.
Bibliographic record
Abstract
Objective: Workplace ethics is a central theme in occupational health; an ethical climate aims to implement and uphold standards of integrity and fairness. Furthermore, the correlation between ethical climate and burnout has been highlighted in several studies, and the impact of a negative ethical climate in the workplace has been reported to affect workers' mental health and job performances, resulting in increased burnout incidence. The aim of this systematic review is to assess the relationship between ethical climate and burnout in the workplace. Method: This review was conducted following the PRISMA statements. Three databases were screened, including research articles written in the English language during the last 10 years, investigating the relationship between burnout and ethics in the workplace. The quality of articles was assessed with the Newcastle-Ottawa Scale. Results: 1153 records were found across three databases; after duplicate removal and screening for title and abstract, 46 manuscripts were screened by full text, resulting in 13 included studies. The majority of the included studies were performed on healthcare workers (n=7, 53.8%), and with a majority of female participants (n=9, 69.2%). Most of the included studies (n=9, 69.2%) evaluated the correlation between ethical climate and burnout, while the other four (n=4, 30.8%) evaluated ethical leadership. Four studies reported a positive correlation between ethics and work engagement. Two studies highlighted that an ethical workplace climate reduced turnover intention. Conclusions: Ethical climate plays an important role in burnout mitigation in workers and in improving work engagement, thus helping to reduce turnover intentions. Since all of these variables have been reported to be present in clusters of workers, these aforementioned factors could impact entire workplace organizations and their improvement could lead to a better work environment overall, in addition to improving the single factors considered. Further studies are needed to investigate the role of ethical climate in the workplace.
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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".