Prenatal control and its impact on reducing maternal deaths: Trend analysis, 1994-2004. Cali, Colombia
Bibliographic record
Abstract
Objective: analysing the statistical data regarding maternal deaths in Cali (Colombia) during two consecutive decades (1985-2004) and correlating outcomes with environmental and social indicators. Methodology: the Canadian Laframboise conceptual model was used for explaining how health service organisation and other aspects of medical care play a part in decreasing maternal mortality rates. Results: maternal deaths in Cali have been decreasing since 1994. No significant quantitative variations in environmental and/or social indicators for Cali were detected from 1994-2004. High prenatal control (97%) and institutional delivery coverage (98%) remained stable, together with an efficient referral and counter-referral system. Improving prenatal attention quality through periodic evaluation and adjustment and obstetrician/gynaecologists ongoing participation in first-level attention were the highlights of public attention service network intervention. Discussion: prenatal risk factors, broad institutional coverage for a quality motherhood care programme and an efficient referral system have all contributed towards reducing maternal deaths by nearly 80 (taking Guzmans 1986 calculation as reference point).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".