Conocimiento y cumplimiento de madres de menores de 5 años sobre esquema de vacunación: Asentamiento San Expedito, Paraguay 2024
Bibliographic record
Abstract
Introduction: Vaccination schedules are evidence-based recommendations that allow populations to prevent communicable diseases in different age groups through immunization. Objective: To determine the knowledge and compliance with vaccination schedules among mothers of children under 5 years of age in the San Expedito settlement, San Lorenzo, Paraguay, 2024. Methodology: A descriptive, observational, non-experimental, quantitative study was conducted. The study population consisted of mothers of children under 5 years of age. The sample included 40 mothers selected through non-probabilistic sampling. Data were collected through interviews using a structured questionnaire. Information was tabulated in Microsoft Excel 2010 and analyzed with EpiInfo 7.2.0.1. Tables and descriptive graphs were generated. Results: Participants ranged in age from 18 to 36 years (mean 27). More than half of the children were infants. Half of the mothers had completed secondary education, a quarter were housewives, and over 10% worked as cashiers. More than half of the households reported incomes below the minimum wage. Regarding knowledge, more than half of the mothers lacked information about vaccination. However, more than half complied with the vaccination schedule according to the child’s age. Conclusion: Limited knowledge about vaccination was observed among mothers, although compliance with the vaccination schedule was above 50%. These findings highlight the need to strengthen education and awareness strategies in the community
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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 | 0.000 | 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) | 0.003 | 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".