Association of Socio-demographic Attributes with Mothers Knowledge regarding Childhood Diarrhea
Bibliographic record
Abstract
Background: Childhood diarrheal diseases have a major impact on morbidity and mortality and these deaths are due to dehydration and mismanagement or delayed management of the disease. The mothers’ knowledge in management of diarrhea is likely related to its mortality and morbidity. The study aimed to determine the association between socio demographic attributes and mother’s knowledge on childhood diarrhea. Materials and Methods: In this cross sectional analytic study, 170 mothers who had at least one child aged below five years old were selected purposively from the out-patient department of ICDDR,B, Dhaka. Data were collected using a structured questionnaire by face to face interview. The level of knowledge was categorized as poor, average and good. Univariate and bivariate analysis were done with level of significance P<0.05. Results: The mean age of the respondents was 27 (SD=±5.6) years. Among them 46.5% were educated up to primary level and 47% had average monthly income between 5001 and 10000 taka. In the case of accessibility to mass media, 20% were found who never watch TV, 75.9% participants were found who never listen to radio, and 87.6% were found who never read newspaper. Despite the level of average knowledge was 59.5% but the proportion of the level of good knowledge was 17% among the respondents. Socio-demographic characteristics such as age, education and income (p<0.001) were significantly associated with mothers knowledge. Conclusion: The mothers had inadequate knowledge about diarrhea and their socio-demographic parameters are strongly associated with mother’s knowledge regarding diarrhea.
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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 | 0.003 |
| 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) | 0.005 | 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".