Determinants of bed net policy implementation: A case study of Southern Benin
Bibliographic record
Abstract
BACKGROUND: Malaria is a major global health challenge. This study aims to clarify the manner in which contextual factors determine the use and maintenance of bed nets and the extent to which malaria prevention policy is responsive to them in Southern Benin. METHODS: Semi-structured interviews and direct observations were undertaken with 30 pregnant women in the municipality of So-Ava from June to August 2015. Key informants in the Ministry of Health and local community health workers were also interviewed regarding malaria prevention policy formation, and the monitoring and evaluation of bed net interventions, respectively. Data were analyzed through categorical content analysis and grouped into themes. RESULTS: The majority of pregnant women participants (80%) declared non-adherence to instructions for hanging and maintaining insecticide-treated nets (ITNs). The distributed bed nets were washed like clothes, which affected their bio-efficacy, and were in poor condition (ie, torn or had holes). Multiple factors contributed to the poor condition of ITNs: Pregnant women's limited understanding of risk including their inability to connect the key environmental factors to personal risk, gendered responsibility for installing bed nets, and lack of public measures that would enable women to re-treat or access new bed nets as needed. Poverty that determined structural aspects of housing such as the size and quality of homes and access to bed nets exacerbated the challenges. CONCLUSION: Institutionalizing an iterative process of monitoring, review, and responsive adaptation throughout the entire policymaking cycle would better support malaria preventive policy implementation in Benin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".