Effectiveness of implementing evidence-based guidelines on nurses' knowledge regarding caring of aborted women
Bibliographic record
Abstract
Nurses and midwives are on the frontlines of the health care workforce, so they should have evidence knowledge to deliver the care and information in a therapeutic, nonbiased manner to the aborted woman. Aim: This study aims to evaluate the effectiveness of implementing evidence-based guidelines on nurses' knowledge regarding caring for aborted women. Subjects and Methods: Quasi-experimental research design (one group pre-test, post-test) was utilized to fulfill the aim of this study. Setting: This study was conducted in obstetrics, labor, and gynecological departments at Minia university hospital for maternity and child. Sample: convenient sample included 55 nurses. Tool: A self-administered questionnaire was used. Results: The study's main findings revealed that nearly three-quarters of the studied nurses (72.7%) had a poor level of total knowledge regarding abortion before implementing evidence guidelines. However, 89.1% and 74.6% of them had good knowledge in immediate and Post three months after evidence guidelines, respectively, with statistically significant differences between pre and post-implementation of evidence-based guidelines in which p-value < 0.001. In addition, there was a statistically significant relation between pre-test knowledge of nurses with their age and year of experience in which P-value ≤ 0.0009 & 0.0003 respectively. Conclusion: Implementing evidence-based guidelines effectively significantly improved nurses' knowledge of caring for aborted women. Recommendation: The nursing curriculum should include updated evidence-based knowledge in abortion care and the distribution of brochures and pamphlets for nurses as guidance concerning abortion.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".