Prevalence of Iron Deficiency Anemia among Adolescent Girls in the City of Saravan
Bibliographic record
Abstract
Background and Objective: Iron deficiency anemia (IDA) is the most common type of micronutrient deficiency in the world. Numerous reports indicated that adolescence is a period which has an increased risk of development of IDA. Given the importance of IDA and lack of studies in Saravan, a city of Iran, this study was performed to assess the prevalence of IDA among adolescent girls.Methods: In this cross-sectional study, 460 high-school girls were randomly selected. Demographic data was collected using a questionnaire. Knowledge, attitude and practice of participants with regard to iron deficiency anemia was measured at the beginning of the interview. Accordingly, five cc of blood sample was drawn from each student to determine the prevalence of anemia, which was defined by a hemoglobin level lower than 12 mg / dL. The level of ferritin was analyzed to confirm the IDA for students diagnosed with anemia. Ferritin level lower than 12 ?g / dl was considered as IDA. Data was analyzed using SPSS software version 22.Results: Prevalence of anemia and IDA was 24% (n = 111) and 12.6% (n = 58), respectively. Results showed that 37 % of students had good knowledge, 45. 5 % good attitude, and 6.7 % had good practice. Also, there was no significant association between IDA and socio-economic status including parental education, job, and household income (P>0.05).Conclusion: The findings of this study showed that the prevalence of IDA was moderate in Saravan city. Given the importance of IDA and its complications, further studies are needed, especially in high risk populations for IDA such as children.
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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 | 0.001 | 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) | 0.001 | 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".