A multi-criteria decision-making approach for prioritizing factors influencing healthcare workers' safety performance: A case of a women's hospital
Bibliographic record
Abstract
This study was designed to examine the influence of organizational and individual-level factors on the safety performance of healthcare workers at a women's hospital. Healthcare workers in different occupational groups enrolled in the current study. A questionnaire was used for data collection, and the data were analyzed using an integrated multi-criteria decision-making (MCDM) approach. The entropy method was applied to prioritize influential factors, including safety climate, perceived organizational support for safety, perceived supervisor support for safety, safety voice, organizational resilience, and individual resilience, and the technique for order preference by similarity to an ideal solution (TOPSIS) was employed to rank the alternatives (healthcare workers in different occupational groups). The finding of the entropy method illustrated that perceived organizational support for safety and organizational resilience had the highest influence on healthcare workers' safety performance. The other most influential factor was individual resilience. Regarding safety performance components, safety compliance was more important than safety participation. TOPSIS results suggested that radiologists, nurses, and midwives experienced higher safety performance levels than the other occupational groups in the study women's hospital. The findings of this study demonstrated that organizational and individual-level factors such as safety climate, perceived organizational and supervisor support for safety, and resilience-related factors significantly influence healthcare workers' safety performance, and compliance with safety rules and procedures is essential to achieve better safety performance.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".