Progress towards the Millennium Development Goals in a community of extreme poverty: local <i>vs.</i> national disparities in Peru
Bibliographic record
Abstract
OBJECTIVES: Standard indicators are being used worldwide to track progress towards achieving the Millennium Development Goals (MDGs). These are usually at country level and do not accurately reflect within-country variability of progress towards the targets. This may lead to lack of attention and under-resourcing of the most vulnerable populations. Therefore, the objective of this study was to compare selected standard MDG indicators at country level and community level in Peru. METHODS: As MDG indicators we selected: (i) moderate to severe and severe underweight in children under 5 years old; (ii) immunization against measles in 1-year olds; (iii) births attended by skilled health professionals and (iv) youth unemployment. Country-level data for Peru were obtained from United Nations published sources. Community-level data were obtained from a household survey conducted in 2005-2006 in Belén, a community of extreme poverty in the Amazon region. RESULTS: Belén indicators were consistently less favourable than country-level indicators, and indicators even differed between zones of high and low socioeconomic status within Belén itself. CONCLUSIONS: Compared to MDG indicators at the national level in Peru, the population of Belén experiences intra-country regional disparities in important health and social outcomes. Improving the coverage and quality of interventions and services in this community is essential. Other vulnerable populations in Peru should also be identified and targeted so that they can benefit from, and ultimately contribute to, progress in achieving the MDGs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".