A knowledge translation toolkit for maternal health implementation planning in low- and middle-income countries: development and pilot evaluation in two countries
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) approaches have been advocated to increase uptake of evidence to improve maternal health outcomes in low- and middle-income countries (LMICs). However, their use is limited by lack of KT capacity and limited applicability of many existing KT tools to the unique challenges of LMIC health settings. We developed and evaluated a toolkit designed for use by non-experts and tailored to support implementation planning in LMICs. METHOD: Based on our prior research which identified common implementation barriers across five LMICs, a literature review and a qualitative study with women and families in two LMICs, we developed a preliminary item list. Through consultation with our international partners, the item list was refined, a draft toolkit developed and usability tested.Pilot evaluation of the toolkit employed observation and focus groups with participants at implementation planning meetings conducted in Argentina and Ghana, focused on locally identified evidence-based maternal health implementation priorities. RESULTS: 31 interested parties participated, 10 in Argentina and 21 in Ghana, representing a range of roles relevant to implementation in the local contexts including providers, health educators, policy/decision makers, researchers and patients/patient representatives. Participants reported a number of benefits to the content and organisation of both the toolkit and meeting format, which they noted encouraged open exchange of perspectives and experiences, and comprehensive consideration and discussion of barriers and facilitators (BFs) to implementation in their context. Minor changes to the instructions and wording of a few BFs were suggested and incorporated. CONCLUSION: The toolkit provides a resource to support LMIC maternal health implementers by offering a structured approach to assessment and ranking of BFs to implementation and a guide to mapping BFs to evidence-based implementation strategies. Further evaluation across a wider range of health topics and LMICs and in low-resource contexts in high-income countries is needed. STUDY REGISTRATION: https://osf.io/328dy Registered 31 May 2023.
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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 | 0.047 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".