Improving the utilization of insecticide-treated nets for malaria prevention among pregnant women, lactating mothers and children in Sierra Leone: a commentary
Bibliographic record
Abstract
Malaria in pregnancy poses significant public health challenges with severe consequences for mothers, fetuses, and newborns. Despite the proven efficacy of insecticide-treated nets (ITNs), the coverage rate among pregnant women, lactating mothers and young children in sub-Saharan Africa remains suboptimal. For example, in Sierra Leone, only 52% of pregnant women and 50% of children under five years utilize ITNs. This coverage rate fell short of the national target, in which at least 80% of pregnant women are expected to report sleeping under an ITN. While considerable research has examined ITN access and usage in the general SSA population, focused implementation research on these high-risk groups in Sierra Leone is notably lacking. Addressing this gap is vital for enhancing intervention effectiveness and achieving sustained malaria control. The authors of this commentary recommend that further implementation research is needed to investigate the barriers and enabling factors to ITN adoption and utilization in pregnant women, lactating mothers and children under five years of age. Implementation research is crucial for understanding the gap between ITN access and actual use, enabling the design of effective and equitable interventions to boost utilization rates. Implementation research anchored in frameworks like Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) offers a pathway to decode these complexities, ensuring that global strategies resonate with local realities. By centering the voices of pregnant women, lactating mothers, and caregivers as well as addressing structural, cultural, and logistical barriers, Sierra Leone can transform ITN coverage into tangible reductions in malaria morbidity and mortality, advancing equity in its march toward elimination.
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 | 0.009 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.027 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".