Factors affecting anemia among women of reproductive age in Mexico: a mixed-methods country case study
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
BACKGROUND: In Mexico, anemia prevalence among women of reproductive age (WRA) decreased from 16.4% in 2006 to 11.6% in 2012, only to increase to 18.3% in 2016. The factors associated with this fluctuation are uncertain. OBJECTIVES: We conducted a systematic in-depth assessment of the quantitative and qualitative determinants of anemia among WRA in Mexico between 2006 and 2018. METHODS: Using multivariate stepwise linear regression, we analyzed Mexico's Encuesta Nacional de Salud y Nutrición surveys from 2006, 2012, and 2018 to identify determinants of WRA anemia. We also conducted a review of anemia-relevant programs and policies, including financing documents, and conducted in-depth interviews and focus group discussions with key stakeholders in Mexico. RESULTS: Among nonpregnant women (NPW) 15-49 y, mean hemoglobin (Hb) increased from 13.8 g/dL in 2006 to 14.0 g/dL in 2012, decreasing to 13.2 g/dL in 2018 (P < 0.001). Inequities by geographical region and household wealth persisted throughout this period, with household wealth, urban residence and gravidity emerging as significant predictors of Hb among NPW. Qualitative analyses generally supported these findings. The most discussed program was Progresa-Oportunidades-Prospera, where most resources for health were invested and majority of participants acknowledged that its cancellation in 2019 would lead to worsening of health and nutrition outcomes among the poor. Financing analyses showed a drop in funding for nutrition-related programs between 2014 and 2018. Cultural norms around gender roles were still prevalent, along with increasing rates of teenage pregnancy. CONCLUSIONS: Anemia prevention efforts need to refocus on poverty alleviation, continuity of adequate coverage and financing of nutrition programs, especially those with social safety nets, and increase in uptake of family planning, particularly among adolescent girls.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".