Preterm birth: the role of knowledge transfer and exchange
Bibliographic record
Abstract
BACKGROUND: Preterm birth (PTB) is the leading cause of death in children under age five. Healthcare policy and other decision-making relevant to PTB may rely on obsolete, incomplete or inapplicable research evidence, leading to worsened outcomes. Appropriate knowledge transfer and exchange (KTE) strategies are an important component of efforts to reduce the global PTB burden. We sought to develop a 'landscape' analysis of KTE strategies currently used in PTB and related contexts, and to make recommendations for optimising programmatic implementation and for future research. METHODS: In the University of California, San Francisco's Preterm Birth Initiative, we convened a multidisciplinary working group and examined KTE frameworks. After selecting a widely-used, adaptable, theoretically-strong framework we reviewed the literature to identify evidence-based KTE strategies. We analysed KTE approaches focusing on key PTB stakeholders (individuals, families and communities, healthcare providers and policymakers). Guided by the framework, we articulated KTE approaches that would likely improve PTB outcomes. We further applied the KTE framework in developing recommendations. RESULTS: We selected the Linking Research to Action framework. Searches identified 19 systematic reviews, including two 'reviews of reviews'. Twelve reviews provided evidence for KTE strategies in the context of maternal, neonatal and child health, though not PTB specifically; seven reviews provided 'cross-cutting' evidence that could likely be generalised to PTB contexts. For individuals, families and communities, potentially effective KTE strategies include community-based approaches, 'decision aids', regular discussions with providers and other strategies. For providers, KTE outcomes may be improved through local opinion leaders, electronic reminders, multifaceted strategies and other approaches. Policy decisions relevant to PTB may best be informed through the use of evidence briefs, deliberative dialogues, the SUPPORT tools for evidence-informed policymaking and other strategies. Our recommendations for research addressed knowledge gaps in regard to partner engagement, applicability and context, implementation strategy research, monitoring and evaluation, and infrastructure for sustainable KTE efforts. CONCLUSIONS: Evidence-based KTE, using strategies appropriate to each stakeholder group, is essential to any effort to improve health at the population level. PTB stakeholders should be fully engaged in KTE and programme planning from its earliest stages, and ideally before planning begins.
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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 | 0.106 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".