Impact of socioeconomic and health related factors on the iron status of adolescent girls from two boarding schools in Southern Benin
Bibliographic record
Abstract
UNLABELLED: To our knowledge, the impact of socioeconomic and health related factors on the iron status of adolescent girls has never been studied in Benin. OBJECTIVE: These factors were studied in 180 girls aged 12 to 17 years living in two boarding schools from South Benin. METHODS: Iron deficiency (ID) was defined as either serum ferritin (SF) < 20 microg/L or SF between 20-50 microg/L plus two abnormal values in the three following parameters: serum iron < 11 micromol/L, total iron binding capacity > 73 micromol/L or transferrin saturation < 20%. Socioeconomic and health related factors were obtained from each participant by mean of an interview using a standardized and pre-tested questionnaire. RESULTS: Almost half the participants reported some health problems in the last four months before the study, whereas more than 75% auto-medicated before going to the hospital. The majority of the girls believed themselves to be in good health, although only 16% could define the term 'anemia'. Multivariate regression analysis indicated that girls whose mother was a manual worker (P = .002), who came from a larger family (P = .0001), and who auto-medicated (P = .014), had a lower hemoglobin level, whereas girls who had started their menstruation (P = .008) had a lower SF level. In a logistic regression analysis, girls from a large family size and whose mother was a manual worker showed a higher risk of IDA (OR = 3.5; 95% CI = 1.1-2.5; P = .04; OR = 3.0; 95% CI = 1.2-2.2; P = .04 respectively). CONCLUSION: The findings indicate that iron deficiency is related to the occupation of the mother, family size, auto-medication, and menstruation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".