Mothers’ Knowledge, Beliefs, and Practices on Causes and Prevention of Anaemia in Children Aged 6 - 59 Months: A Case Study at Mkuranga District Hospital, Tanzania
Bibliographic record
Abstract
Aims: Anaemia in children aged 6 - 59 months is an important public health problem associated with increasing hospital costs, lengths of hospital stay, and development of complications later in life. Due to the significant caregiver roles of mothers, the level of mothers’ knowledge, beliefs and practices are important in addressing anaemia in children. This study investigated knowledge, attitudes, beliefs, and practices of select mothers on anaemia and linked these with known factors for anaemia. Methodology: The case study was conducted at Mkuranga District Hospital, Paediatric Ward between December 2014 and April 2015. A cross-sectional design was used to recruit a convenience sample of 40 mothers whose children had a confirmed diagnosis of anaemia (through routine laboratory testing). Results: Within the sample, the majority of children were male (52.5%); aged 6 - 39 months (87.5%); and had a diagnosis of severe anaemia (75.0%) according to the World Health Organization’s definition. Over one third (35%) of mothers reported a prior history of anemia in their other children, and the majority (55%) had heard about anaemia prior to their child’s hospitalization. Maternal anaemia was reported by 67.5% of mothers. Mothers reported that maternal anemia (17.5%) and feeding practices (32.5%) are known contributing factors to anaemia in children. Mothers reported that anemia could be prevented (55.0%) and cured by herbal preparations (47.5%). In addition, some mothers indicated that anaemia was caused by witchcraft (22.5%) and eating lemons (2.5%). Conclusion: Severe anaemia was high among the studied population which aligned with their hospitalization status. Findings suggested potential gaps in control and management of anemia in children possibly related to low awareness or incorrect knowledge of the relationship between maternal and child anaemia. The findings also highlighted important cultural beliefs related to anaemia. There is an imperative for culturally and socially appropriate knowledge translation and exchange with mothers in order to impact on the prevention and control of anaemia in children in Tanzania.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".