Prioritizing the Compensation Mechanisms for Nurses Working in Emergency Department of Hospital Using Fuzzy DEMATEL Technique: A Survey from Iran
Bibliographic record
Abstract
AIM AND BACKGROUND: Nursing professionals are the most important human resources that provide care in the Emergency Departments at hospitals. Therefore appropriate compensation for the services provided by them is considered as a priority. This study aims to identify and prioritize the factors affecting the compensation for services provided by the EDs nurses. METHODS: Twenty four nurses, hospital administrators, local and national health authorities participated in a cross sectional study conducted in 2012. The participants discussed on different compensation mechanisms for nurses' of EDs, in six groups according to Focus Group Discussion (FGD) technique, resulted in the adopted mechanisms. Opinions of the participants on the mechanisms were obtained via paired matrices using fuzzy logic. Decision Making Trial and Evaluation Laboratory (DEMATEL) technique was used for prioritizing the adopted mechanisms. FINDINGS: Among the compensation mechanisms for nurses of ED services, both Monthly fixed amounts (9.0382) and increasing the number of vacation days (9.0189) had highest importance. The lowest importance was given to the performance-based payment (8.9897). Monthly fixed amounts, increasing the number of vacation days, decreasing the working hours and performance-based payment were recognized as effective factors. Other mechanisms are prioritized as use of the facilities, increase in emergency tariff, job promotions, non-cash payments, continuing education, and persuasive years. CONCLUSION: According to the results, the nurses working in the EDS of the hospitals were more likely to receive non-cash benefits than cash benefits as compensation.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".