Assessing the impact of mentorship on nurses’ and midwives’ knowledge and self-efficacy in managing postpartum hemorrhage
Bibliographic record
Abstract
Background Despite medical technology advancement, postpartum hemorrhage remains the top universal cause of maternal mortality. Factors note the inconsistency in recognition and timely treatment of women experiencing it, which suggests healthcare professionals' mentorship about postpartum hemorrhage. Methods The study recruited 141 nurses and midwives and used instruments adapted to knowledge and self-efficacy to assess the impact of mentorship on nurses' and midwives' knowledge and self-efficacy in managing postpartum hemorrhage. Results There was an increase in knowledge from 68% prior to mentorship up to 87% and self-efficacy from 6.9 to 9.5 average score out of 10. Knowledge and self-efficacy correlated moderately positive at pre-mentorship (r=0.214) and strongly positive at post-mentorship (r=0.585). The number of mentorship visits attended was associated with post-mentorship knowledge scores (r=0.539) and post-mentorship self-efficacy (r=0.623). Conclusions Mentorship about management of postpartum hemorrhage increases nurses' and midwives' knowledge and self-efficacy in managing postpartum hemorrhage.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".