Knowledge translation strategies for policy and action focused on sexual, reproductive, maternal, newborn, child and adolescent health and well-being: a rapid scoping review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify knowledge translation (KT) strategies aimed at improving sexual, reproductive, maternal, newborn, child and adolescent health (SRMNCAH) and well-being. DESIGN: Rapid scoping review. SEARCH STRATEGY: A comprehensive and peer-reviewed search strategy was developed and applied to four electronic databases: MEDLINE ALL, Embase, CINAHL and Web of Science. Additional searches of grey literature were conducted to identify KT strategies aimed at supporting SRMNCAH. KT strategies and policies published in English from January 2000 to May 2020 onwards were eligible for inclusion. RESULTS: (81%), including staff workshops and education modules, was the most commonly identified intervention component from the KT interventions. Low-income and middle-income countries were more likely to include civil society organisations, government and policymakers as stakeholders compared with high-income countries. Reported barriers to KT strategies included limited resources and time constraints, while enablers included stakeholder involvement throughout the KT process. CONCLUSION: We identified a number of gaps among KT strategies for SRMNCAH policy and action, including limited focus on adolescent, sexual and reproductive health and rights and SRMNCAH financing strategies. There is a need to support stakeholder engagement in KT interventions across the continuum of SRMNCAH services. Researchers and policymakers should consider enhancing efforts to work with multisectoral stakeholders to implement future KT strategies and policies to address SRMNCAH priorities. REGISTRATION: The rapid scoping review protocol was registered on Open Science Framework on 16 June 2020 (https://osf.io/xpf2k).
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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 | 0.231 | 0.375 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.033 | 0.028 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".