Effect of community-based newborn care implementation strategies on access to and effective coverage of possible serious bacterial infection (PSBI) treatment for sick young infants during COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: In Ethiopia, neonatal mortality is persistently high. The country has been implementing community-based treatment of possible serious bacterial infection (PSBI) in young infants when referral to a hospital is not feasible since 2012. However, access to and quality of PSBI services remained low and were worsened by COVID-19. From November 2020 to June 2022, we conducted implementation research to mitigate the impact of COVID-19 and improve PSBI management implementation uptake and delivery in two woredas in Ethiopia. METHODS: In April-May 2021, guided by implementation research frameworks, we conducted formative research to understand the PSBI management implementation challenges, including those due to the COVID-19 pandemic. Through a participatory process engaging stakeholders, we designed adaptive implementation strategies to bridge identified gaps using mechanism mapping to achieve implementation outcomes. Strategies included training and coaching, supportive supervision and mentorship, technical support units, improved supply of essential commodities, and community awareness creation about PSBI and COVID-19. We conducted cross-sectional household surveys in the two woredas before (April 2021) and after the implementation of strategies (June 2022) to measure changes in targeted outcomes. RESULTS: We interviewed 4,262 and 4,082 women who gave live birth 2-14 months before data collection and identified 374 and 264 PSBI cases in April 2021 and June 2022, respectively. The prevalence of PSBI significantly decreased (p-value = 0.018) from 8.7% in April 2021 to 6.4% while the mothers' care-seeking behavior from medical care for their sick newborns increased significantly from 56% to 91% (p-value <0.01). Effective coverage of severely ill young infants that took appropriate antibiotics significantly improved from 33% [95% CI: 25.5-40.7] to 62% [95% CI: 51.0-71.6]. Despite improvements in the uptake of PSBI treatment, persisting challenges at the facility and systems levels impeded optimal PSBI service delivery and uptake, including perceived low quality of service, lack of community trust, and shortage of supplies. CONCLUSION: The participatory design and implementation of adaptive COVID-19 strategies effectively improved the uptake and delivery of PSBI treatment. Support systems were critical for frontline health workers to deliver PSBI services and create a resilient community health system to provide quality PSBI care during the pandemic. Additional strategies are needed to address persistent gaps, including improvement in client-provider interactions, supply of essential drugs, and increased social mobilization strategies targeting families and communities to further increase uptake.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".