Operationalizing Implementation Science in Nutrition: The Implementation Science Initiative in Kenya and Uganda
Bibliographic record
Abstract
BACKGROUND: Implementation science (IS) has the potential to improve the implementation and impact of policies, programs, and interventions. Most of the training, guidance, and experience has focused on implementation research, which is only 1 part of the broader field of IS. In 2018, the Society for Implementation Science in Nutrition borrowed concepts from IS in health to develop a broader and more integrated conceptual framework, adapted to the particular case of nutrition and with language and concepts more familiar to the nutrition community: it is called the IS in Nutrition (ISN) framework. OBJECTIVE: The purpose of this research was to generate knowledge concerning challenges and strategies in operationalizing the ISN framework in low- and middle-income country (LMIC) settings. METHODS: The ISN framework was operationalized in partnership with country teams in Kenya and Uganda over a 3-y period as part of the Implementation Science Initiative. An action research methodology (developmental evaluation) was used to provide timely feedback to the country teams, facilitate adaptations and adjustments, and generate the data presented in this article concerning challenges and strategies. RESULTS: Operationalization of the ISN framework proceeded by first articulating a set of guiding principles as touchstones for the country teams and further articulating 6 components of an IS system to facilitate development of work streams. Challenges and strategies in implementing these 6 components were then documented. The knowledge gained through this experience led to the development of an IS system operational model to assist the application of IS in other LMIC settings. CONCLUSIONS: Future investments in IS should prioritize a system- and capacity-building approach in order to realize its full potential and become institutionalized at country level. The operational model can guide others to improve the implementation of IS within a broad range of programs.
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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 | 0.232 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".