Improving maternal and child healthcare programme using community-participatory interventions in Ebonyi State Nigeria
Bibliographic record
Abstract
In Nigeria, the government is implementing the Free Maternal and Child Health Care Programme (FMCHCP). The policy is premised on the notion that financial barriers are one of the most important constraints to equitable access and use of skilled maternal and child healthcare. In Ebonyi State, Southeastern Nigeria the FMCHCP is experiencing implementation challenges including: inadequate human resource for health, inadequate funding, out of stock syndrome, inadequate infrastructure, and poor staff remuneration. Furthermore, there is less emphasis on community involvement in the programme implementation. In this policy brief, we recommend policy options that emphasize the implementation of community-based participatory interventions to strengthen the government's FMCHCP as follows: Option 1: Training community women on prenatal care, life-saving skills in case of emergency, reproductive health, care of the newborn and family planning. Option 2: Sensitizing the community women towards behavioural change, to understand what quality services that respond to their needs are but also to seek and demand for such. Option 3: Implementation packages that provide technical skills to women of childbearing age as well as mothers' groups, and traditional birth attendants for better home-based maternal and child healthcare. The effectiveness of this approach has been demonstrated in a number of community-based participatory interventions, building on the idea that if community members take part in decision-making and bring local knowledge, experiences and problems to the fore, they are more likely to own and sustain solutions to improve their communities' health.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".