Knowledge of Mothers on Factors Associated with Anaemia among Children under Five Years Old in Orile-Agege General Hospital, Lagos, Nigeria
Bibliographic record
Abstract
Anaemia in children under five years old is a public health concern worldwide. In developing countries about 12 million children under five years old die each year from preventable causes. The deaths of over 6 million are either directly or indirectly attributed to malnutrition, mainly under-nutrition that leads to anaemia and constitutes a high percentage of infant and child mortality. This descriptive survey attempted to assess the knowledge of mothers on factors associated with anaemia among children under five years old in the child welfare clinic at Orile-Agege General Hospital, Lagos. The 120 respondents were conveniently selected and data was collected through a close ended question items and analyzed with Pearson Product Moment Correlation. The findings revealed that 111(92.5%) of the respondents agreed that one of the major causes of anaemia was malnutrition. Consequently, respondents agreed that children who suffer from anaemia are prone to infections, delayed psychomotor development, poor academic performance and low scores in intelligent (IQ) tests which deprived them the opportunity to be physically fit and function at optimal level. There was no significant relationship between occurrence of anaemia and mothers’ educational status (r = .29) as well as their socio economic status (r = .091). The religious belief of the respondents also had no bearing with the occurrence of anaemia (r =.152). It was therefore recommended that there is need for more public enlightenment on the causes, prevention and complications of anaemia. Capacity building for health care providers to adequately equip them with updates and facts on the management of prevailing rate of anaemia effectively. Anaemia in children under five years old is a public health concern worldwide. In developing countries about 12 million children under five years old die each year from preventable causes. The deaths of over 6 million are either directly or indirectly attributed to malnutrition, mainly under-nutrition that leads to anaemia and constitutes a high percentage of infant and child mortality. This descriptive survey attempted to assess the knowledge of mothers on factors associated with anaemia among children under five years old in the child welfare clinic at Orile-Agege General Hospital, Lagos. The 120 respondents were conveniently selected and data was collected through a close ended question items and analyzed with Pearson Product Moment Correlation. The findings revealed that 111(92.5%) of the respondents agreed that one of the major causes of anaemia was malnutrition. Consequently, respondents agreed that children who suffer from anaemia are prone to infections, delayed psychomotor development, poor academic performance and low scores in intelligent (IQ) tests which deprived them the opportunity to be physically fit and function at optimal level. There was no significant relationship between occurrence of anaemia and mothers’ educational status (r = .29) as well as their socio economic status (r = .091). The religious belief of the respondents also had no bearing with the occurrence of anaemia (r =.152). It was therefore recommended that there is need for more public enlightenment on the causes, prevention and complications of anaemia. Capacity building for health care providers to adequately equip them with updates and facts on the management of prevailing rate of anaemia effectively.
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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 | 0.001 | 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) | 0.002 | 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".