Correlation between psychophysiological response and hospital management model for nurses involved in medication errors
Bibliographic record
Abstract
Because hospitals in Taiwan now place a lot of emphasis on hospital accreditation, leading to a heavy workload for nurses, nurses are unintentional involved in patient medication errors (MEs). In this research, we explore the possible correlation between psychophysiological responses of nursing staff and a hospital management model regarding MEs. We conducted a cross-sectional study design in one hospital in central Taiwan. A total of 345 nurses at Tali Jen-Ai Hospital l were selected. A questionnaire was chosen as the study instrument. The questionnaire asked the following information: basic data, classification of MEs, frequency and severity of psychophysiological responses, and information about the hospital’s management model and support system. We found that the hospital management model had a significant negative correlation with the frequency of physiological symptoms (r = -0.14, p < .01). Likewise, the support system also had a substantial negative correlation with the scores of the physiological symptom, behavioral responses, and psychophysiological responses. Using multiple regression analysis adjusted for work duration, job title, and degree of injury, we found that the support system was significantly correlated with the nurses’ psychophysiological responses. Both the hospital management and the support system decreased the psychophysiological responses of the nurses. Therefore, we recommend that supervisors implement a management strategy to deal specifically with MEs. When nurse managers have frequently empathy and no-blame attitude to replace disrespectful or skeptical ways to nurse with ME, they will empower their enthusiasm and responsibility to improve the reduction of ME.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".