Evidence‐Based Stillbirth Prevention Strategies: Combining Empirical and Theoretical Paradigms to Inform Health Planning and Decision‐Making
Bibliographic record
Abstract
INTRODUCTION: A global health project undertaken in Qatar on the Arabian Peninsula immersed undergraduate nursing students in hands-on learning to address the question: What strategies are effective in preventing stillbirth? Worldwide stillbirth estimates of 2.6 million per year and the high rate in the Eastern Mediterranean Region of 27 per 1,000 total live births provided the stimulus for this inquiry. METHODS: We used a dual empirical and theoretical approach that combined the principles of evidence-based practice and population health planning. Students were assisted to translate pre-appraised literature based on the 6S hierarchical pyramid of evidence. The PRECEDE-PROCEED (P-P) model served as an organizing template to assemble data extracted from the appraisal of 21 systematic literature reviews ± meta-analyses, 2 synopses of synthesized reports, and 9 individual studies summarizing stillbirth prevention strategies in low, middle, and high income countries. Consistent with elements of the P-P model, stillbirth prevention strategies were classified as social, epidemiological, educational, ecological, administrative, or policy. RESULTS: Ten recommendations with clear evidence of effectiveness in preventing stillbirth in low, middle, or high income countries were identified. Several other promising interventions were identified with weak, uncertain, or inconclusive evidence. These require further rigorous testing. LINKING EVIDENCE TO ACTION: Two complementary paradigms--evidence-based practice and an ecological population health program planning model--helped baccalaureate nursing students transfer research evidence into useable knowledge for practice. They learned the importance of comprehensive assessments and evidence-informed interventions. The multidimensional elements of the P-P model sensitized students to the complex interrelated factors influencing stillbirth and its prevention.
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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 | 0.238 | 0.290 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.027 | 0.011 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".