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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".