Using the Implementation Research Logic Model to design and implement community-based management of possible serious bacterial infection during COVID-19 pandemic in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Community-based treatment of possible serious bacterial infection (PSBI) in young infants, when referral to a hospital is not possible, can result in high treatment coverage and low case fatality. However, in Ethiopia, the coverage of PSBI treatment remains low, worsened by COVID-19. To understand the challenges of delivery of PSBI treatment and design and test adaptative strategies to mitigate the impact of COVID-19 on neonatal mortality, we did implementation research (IR) employing Implementation Research Logic Model (IRLM). In this paper, we describe IRLM application experiences in designing, implementing, and evaluating strategies to improve community-based treatment of PSBI during the COVID-19 pandemic in Ethiopia. METHODS: This IR was conducted between November 2020-April 2022 at Dembecha and Lume woredas of Amhara and Oromia regions, respectively. We employed narrative reviews, formative assessment and facilitated stakeholder engagement to develop the PSBI treatment IRLM to identify barriers, understand the conceptual linkages among determinants, choose implementation strategies, elicit mechanisms, and link to implementation outcomes. In addition, we used the IRLM to structure the capture of emerging implementation challenges and resulting strategy adaptations throughout implementation. RESULTS: This IR identified COVID-19 and multiple pre-existing contextual factors. We designed and implemented implementation strategies to address these challenges. These adaptive strategies were implemented with sufficient strength to maintain the delivery of PSBI services and improve mothers' care-seeking behavior for their sick young infants. The IRLM offers us a clear process and path to prioritize implementation challenges, choose strategies informed by mechanisms of action, and where the adaptive implementation of community-based management of PSBI would lead to high-implementation fidelity and change in mother behavior to seek care for their sick young infants. The IRLM was also an effective tool for stakeholder engagement, easily explained and used to structure discussion and decision-making during co-design meetings. CONCLUSIONS: The use of the IRLM helps us to specify the conceptual links between the implementation challenges, strategies, mechanisms of action, and outcomes to explore the complex community-based management of PSBI during complex contexts to improve high-fidelity implementation and integration of PSBI treatment in the primary healthcare delivery systems through active engagement of stakeholders.
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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 | 0.105 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".