Impact of a Positive Deviance Approach to Improve the Effectiveness of an Iron-Supplementation Program to Control Nutritional Anemia among Rural Senegalese Pregnant Women
Bibliographic record
Abstract
BACKGROUND: Iron supplementation through prenatal care remains the most widespread strategy to control anemia during pregnancy, but its effectiveness is only partial, showing the need to address other approaches. OBJECTIVE: This study was conducted to measure the impact of a positive deviance approach to improve an iron-supplementation program among pregnant women in a rural Senegalese area. METHODS: A positive deviance approach (PD Micah) was compared with an ongoing integrated nutrition and health program intervention (Micah) in a rural Senegalese area. A pre-post evaluation was conducted using independent cross-sectional samples with a total of 371 pregnant women. A sociodemographic questionnaire was administered, and biologic and anthropometric measurements were performed. RESULTS: After 9 months of activities, the mean hemoglobin level rose from 93.9 to 100.7 g/L in the PD Micah group. Distribution of iron supplements through community volunteers and implementation of healthy pregnancy promotion sessions on a monthly basis improved the accessibility to 23.3% in the PD Micah group. No significant change was observed in the Micah group. Logistic regression analysis showed a significantly reduced risk of anemia in the PD Micah area (adjusted odds ratio, 0.25; 95% confidence interval, 0.12 to 0.53). CONCLUSIONS: This intervention shows that a community-based strategy, such as the positive deviance approach, can contribute to improving the effectiveness of iron supplementation during pregnancy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".