A hybrid approach for analyzing and assessing resilience engineering in healthcare
Bibliographic record
Abstract
BackgroundToday, researchers try to recognize and improve the weaknesses of complex systems using resilience engineering (RE) principles.ObjectiveThis descriptive-analytical epidemiological study aims to evaluate the resilience performance of the hospital staff based on the principles of RE to enhance personnel performance.MethodA questionnaire containing 27 questions in four areas (anticipation, monitoring, response, and learning) was designed based on Hellangel's model and field surveys to collect data from nurses and managers of various wards of five hospitals. The face validity and reliability of the questionnaire were confirmed by 16 professors and some individuals from the statistical sample.A hybrid approach utilizing entropy, TOPSIS, and DEA was employed in the Excel environment to process the collected data.ResultsThe results of the entropy method indicated that the responding and monitoring indicators, with values of 0.29 and 0.23, respectively, had the greatest impact on the resilience performance of the studied units. The outcomes of TOPSIS revealed that hospital D possesses the highest level of resilience. According to the DEA method, the first, second, and fifth efficient units were associated with hospital B.ConclusionBy evaluating the cornerstone of resilience, this study's findings empower nurses and managers to mitigate the impacts of stress, emotional exhaustion, and burnout, fostering work interactions and improving their performance in the face of workplace challenges. The main limitation of the study was the spread of COVID-19, which impeded the training of personnel in the field of RE and its indicators, as well as the distribution, collection, and completion of questionnaires, along with challenging access to personnel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".