An MCDM approach to assessing influential factors on healthcare providers’ safe performance during the COVID-19 pandemic: Probing into demographic variables
Bibliographic record
Abstract
This study assessed the influence of occupational stress, individual resilience, and organizational resilience on the safety performance of healthcare providers during the COVID-19 pandemic. Demographic variables including age, work experience, and gender were explored. Data were collected from 344 healthcare providers employed at a teaching hospital. The entropy method and the multi-criteria decision-making (MCDM) method were used to examine the influence of occupational stress, individual resilience, and organizational resilience on the safe performance of healthcare providers. The results of the entropy method showed that organizational resilience was the most influential factor in the safe performance of older healthcare providers. In contrast, individual resilience was the most significant factor in enhancing the safety performance of younger healthcare providers. Analyses of work experience indicated that individual resilience was the most influential factor in the safe performance of less experienced healthcare providers. Gender-based analysis revealed that individual resilience had a major effect on the safety performance of both women and men. The findings of this study could assist managers in improving the performance of the healthcare sector during pandemics by using and implementing resilience concepts at both the individual and organizational levels.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".