The Effect of Education and Implementation of Evidence-Based Nursing Guidelines on Infants’ Weight Gaining in NICU
Bibliographic record
Abstract
BACKGROUND: Educating evidence-based guidelines influences increased quality of nursing cares effectively. Infant's weight gaining is one of the most important indicators for measuring quality of nursing care in NICU. The research is conducted with the aim of surveying the effect of education and implementation of educating evidence-based guidelines on infants' weight gaining in NICU. METHODS: This two-group clinical trial study was conducted in 2013 on one hundred infants in Baqiyatallah (AJ) hospital of Tehran. It was performed by using non-probable and convenient sampling. Data collection tools included; infants' demographic questionnaire and a researcher-made checklist to record infants' weight by using a weighing scale. Infants' weight was recorded before intervention and two months after implementation of the guidelines, then data were analyzed by using SPSS19 statistical software. FINDINGS: Mean weight of the infants in the control group on admission and on discharge was respectively; 1771(41.71) and 1712(42.68), and mean weight of the infants in intervention group on admission and on discharge was respectively; 1697(37.63) and 1793(40.71). After two months, infants' weight gaining in intervention group was more than control group and it was statistically significant (P = 0.001). CONCLUSION: results of the present study showed that implementation of evidence-based instruction an effective and economical method regarding infants' weight gaining. Therefore it is recommended to the authorities and managers of the hospitals and educational centers of the healthcare services to put education and implementation of educating evidence-based instruction the priority of their work plans.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".