Newborn’s body temperature regulation: a multi-method training program
Bibliographic record
Abstract
Introduction: Newborns’ incapability to self-regulate their body temperature exposes them torisk of many complications. Therefore, proper initiations of nursing interventions are crucial for ensuring early stabilization after birth. The present study aimed to evaluate the impact of a multi-method training program on knowledge and practices in neonatal nurses. Methods: This quasi-experimental research was carried out with a pre-test–post-test and intervention-control groups design. The population consisted of 60 nurses working in the neonatal and neonatal intensive care unit (NICU) of two hospitals in Iran. The samples were selected using the convenience sampling technique. A researcher-made questionnaire was used. The validation and reliability of the questionnaire were confirmed. The nurses received a multi-method training for two weeks. They were evaluated three months later. Results: The mean knowledge score obtained in the pre-test was 30.33 ± 2.01, and it was47.73 ± 6.82 three months after the intervention. There was a statistically significant difference in the trend of changes in the scores of the two groups (F = 125.72, P < 0.001). The mean performance score in intervention group before the intervention was 60.63 ± 5.69 and after the intervention was 81.77 ± 6.37, indicating a significant difference in the trend of changes(F = 90.285, P < 0.001).The correlation between knowledge and practice was significant at the 0.01 level (r = 0.544,P = 0.002). The previous in-service training, individuals’ education, and thermoregulation knowledge were determined as predictors of performance (r = 0.651, P = 0.013). Conclusion: According to the results, a multi-method training program improves the nurses’ knowledge and performance in the infants’ body temperature regulation care.
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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 | 0.041 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| 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; both teacher heads agree on what is shown here.
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".