Challenges in last mile distribution of family planning commodities: Effects on product availability and accessibility in Nigeria
Bibliographic record
Abstract
BACKGROUND: Family planning (FP) reduces maternal and child mortality risk. Despite policies and plans to improve FP in Nigeria, access remains poor leading to high unmet need. Contraceptive use is still as low as 4.9% in some regions. Thus, this study assessed challenges in FP commodities distribution and its effect on accessibility. METHODS: Descriptive survey was used to explore last mile distribution of FP commodities in 287 facilities across various levels of FP service provision. Also, 2528 end users of FP services were assessed to ascertain their attitudes towards FP services. Data were analysed using IBM Statistical Package for the Social Sciences version 25. RESULTS: Only 16% of the facilities had all the basic infrastructure requirements assessed with majority of the facilities having inadequate human resource capacity on logistics and supply chain management of health commodities. The study also identified positive attitudes towards FP (80%) and low incidence of stigmatising attitudes (5.4%). CONCLUSIONS: The study identified challenges in distribution of FP commodities including frequent stock out of commodities and socio-cultural barriers. Increased positive attitude and limited stigmatising attitudes provides policy directions that are relevant for decision makers to align FP policies and strategies to improve last mile distribution of FP commodities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".