Health in conflict and post-conflict settings: reproductive, maternal and child health in Colombia
Bibliographic record
Abstract
BACKGROUND: In conflict-afflicted areas, pregnant women and newborns often have higher rates of adverse health outcomes. OBJECTIVE: To describe maternal and child health indicators and interventions between 1998 and 2016 comparing high and low conflict areas in Colombia. METHODS: Mixed study of convergent triangulation. In the quantitative component, 16 indicators were calculated using official, secondary data sources. The victimization rate resulting from armed conflict was calculated by municipality and grouped into quintiles. In the qualitative component, a comparative case study was carried out in two municipalities of Antioquia: one with high rates of armed conflict and another with low rates. A total of 41 interviews and 8 focus groups were held with local and national government officials, health professionals, community informants, UN agencies and NGOs. RESULTS: All of the indicators show improvement, however, four show statistically significant differences between municipalities with high victimization rates versus low ones. The maternal mortality ratio was higher in the municipalities with greater victimization in the periods 1998-2004, 2005-2011 and 2012-2016. The percentage of cesarean births and women who received four or more antenatal visits was lower among women who experienced the highest levels of victimization for the period 1998-2000, while the fertility rate for women between 15 and 19 years was higher in these municipalities between 2012 and 2016. In the context of the armed conflict in Colombia, maternal and child health was affected by the limited availability of interventions given the lack of human resources in health, supplies, geographical access difficulties and insecurity. The national government was the one that mostly provided the programs, with difficulties in continuity and quality. UN Agencies and NGOs accessed more easily remote and intense armed conflict areas. Few specific health interventions were identified in the post-conflict context. CONCLUSIONS: In Colombia, maternal and child health indicators have improved since the conflict, however a pattern of inequality is observed in the municipalities most affected by the armed conflict.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".