Evidence of Resiliency in Maternal Health Services and Outcomes in Kono District, Sierra Leone during the COVID-19 Pandemic: An Observational Study
Bibliographic record
Abstract
Abstract Background This study evaluated the resilience and outcomes of maternal health services during the COVID-19 pandemic in two health facilities in eastern Sierra Leone. Its aims to describe the use of maternal healthcare services and maternal and neonatal outcomes in these two facilities before, during, and after the COVID-19 pandemic. Methods The study involved analysis of routine programme data (March 2019 to February 2022) from two public-funded health facilities supported by a non-governmental organization (Partners In Health in Sierra Leone): Koidu Government Hospital and Wellbody Clinic. Aggregated and de-identified secondary data from the Partner In Health Maternal Health Database was abstracted using a standardized tool. Descriptive statistics and bivariable negative binomial regression were used to assess the association between time periods (before COVID-19 [March 2019 to February 2020], during COVID-19 emergency [March 2020 to February 2021], after COVID-19 emergency [March 2021 to February 2022) and outcomes each month (fourth antenatal care visit and facility deliveries). Results The study analyzed 3,204 fourth antenatal care visits and 7,369 deliveries over 36 months at both health facilities. Fourth antenatal care visits (from 947 to 920) and facility deliveries (from 2309 to 2221) decreased during COVID-19 compared to pre-COVID-19. However, maternal (from 32 to 23) and neonatal (36 to 26) deaths declined during COVID-19 compared to the pre-COVID-19 period at Koidu Government Hospital. Conclusion In Sierra Leone, the resources and efforts directed to the post-Ebola recovery strategy were tested during and after the COVID-19 pandemic. Our study demonstrates the resilience of maternal and neonatal services in two healthcare facilities in a less-affected region of Sierra Leone, to the anticipated disruptions due to the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".