Knowledge, Attitudes and Practices of Mothers Concerning Fever among Children under Five Years of Age in the Mabulu 2 Quarter in Kinshasa
Bibliographic record
Abstract
Purpose: This exploratory study aims to improve the knowledge, attitudes and practices of mothers in the face of fever in children under five years old. Material and Methods: A cross-sectional study carried out from 1 to 30 November 2022 among 36 mothers with children under five years old in the Mabulu 2 quarter, using a questionnaire is analyzed using the jamovi 2.3.28 software. Descriptive analyses, Fisher's exact test and Spearman's correlation ( = 0.05) were used. Results: Knowledge: 94.4% of mothers consider fever to be an illness. 66.7% believe that the best temperature sampling site is the armpit, which the first step to take is to administer medication and know that we can use a wet wrap and paracetamol. Attitudes: 55.6% go to the pharmacy; 69.4% prefer to combine the antipyretic and the antibiotic; 75% maintain that an overdose makes it possible to obtain a rapid drug effect and 97.2% are for a dose adapted to the child's weight. Practices: 69.4% of mothers evaluate the fever by touch; 52.8% evaluate the effects of the intervention at more than 2 hours; 80.6% use the teaspoon for the dose and 66.7% use antibiotics without a doctor's prescription. Attitudes were associated with level of education (p = 0.003) and compliance with the vaccination schedule (p = 0.035). The practices depended on the age of the mothers (p = 0.014). A positive correlation was found between knowledge, attitudes and practices (p < 0.01). Conclusion: It is useful to educate mothers in the Mabulu 2 neighborhood on fever in order to improve their knowledge, attitudes and practices when it occurs in toddlers.
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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 | 0.000 | 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".