A Systematic Approach for Midwifery Students: How to Consider Evidence‐Based Research Findings
Bibliographic record
Abstract
The midwifery profession is increasingly applying the results of evidence-based research findings. Several researchers were asked if they would answer questions regarding the essential research skills necessary for midwives, the relevance of applying valid evidence to practice, and concerns regarding evidence-based practice overall. The objectives were to share expert researchers' responses that could be used by educators to help introductory midwifery students understand the importance of developing skills in assessing "the best evidence" and to stimulate interactive discussion in the classroom. Consideration of the expert opinions stimulated student thinking on the relation of evidence-based findings to practice in an exciting approach characterized by inquiry and debate, which got favorable responses and evaluations from the students.
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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 | 0.187 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".