A policy analysis of policies and strategic plans on Maternal, Newborn and Child Health in Ethiopia
Bibliographic record
Abstract
Significant progress has been made to advance Maternal, Newborn and Child Health (MNCH) in Ethiopia. Further, the country has enshrined equity as a core value in their strategic and development frameworks and policies. Although national statistics show improved health outcomes, there exists persistent inequities in avoidable health risks and premature deaths. Additionally, the improving health statistics mask the disparities in health outcomes based on education, employment status, income level, gender and ethnicity dimensions.The EquiFrame framework was used to assess the extent to which equity was entrenched in MNCH health policies and plans. The framework, which describes core concepts against which health policies and plans can be assessed, also provides a scoring criterion for policy assessment. The framework was modified to include the concept of intersectionality, which is increasingly gaining significance in the health policy ecosystems. The policies and plans reviewed in this analysis exercise were selected based on (1) their relevance - only policies and plans in force as of the year 2020 were considered; (2) availability in the public domain as this study was limited to desk research; and (3) relevance to MNCH. A total of five policies and plans were analyzed and evaluated against the 15 core concepts presented in the modified EquiFrame framework. Following the outcomes of the assessment, documents were ranked as either being low, moderate, or high, in exhaustively addressing the core concepts.The Ethiopia Health Sector Transformation Plan (2016-2020) is the only policy or plan that earned a high ranking. The other four policies and plans were ranked as moderate. This shows that while majority of the Ethiopian health sector policies and plans exist and address the core health equity concepts, they fail to: (i) spell out plans to implement and monitor the proposed interventions; and (ii) demonstrate evidence that the interventions were implemented or monitored. With the global goal of leaving no one behind, future policy development in Ethiopia needs to prioritize equity considerations in order to enhance the ongoing health improvement.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".