FACTORS RELATED TO KNOWLEDGE ON NEWBORN DANGER SIGNS AMONG THE RECENTLY DELIVERED WOMEN IN SUB-DISTRICT HOSPITALS OF BANGLADESH
Bibliographic record
Abstract
Background: Bangladesh continues to be one of the top ten countries with the highest burden of neonatal mortality. While, most of the neonatal deaths are preventable; health system delays, delayed identification of newborn danger signs, late diagnosis and initiation of treatment are claimed to be the main challenges.Objective: 1) to determine the level of knowledge among the recently delivered women (RDW) about newborn danger signs and 2) to distinguish the factors associated with ability of identifying the danger signs.Methods: A facility based cross-sectional study was conducted in three sub-district hospitals of Bangladesh among 135 RDW between 1 January 2015 and 30 April 2015. Seven key danger signs were identified, and responses were categorized accordingly. Bivariable logistic regression was conducted to determine the likelihood of the association of factors with danger signs identification.Results: About 51% of RDW could identify one key danger sign. Knowledge on “fever’’ was the most commonly known danger sign (65%). Middle age (OR 1.67, 95% CI: 1.09 - 2.18), high education (OR 2.37, 95% CI: 1.46 - 2.77), increased parity (OR 1.91, 95% CI: 1.17 - 2.89), and previous hospital delivery (OR 1.79, 95% CI: 1.14 - 2.68) were found associated with the knowledge of the danger signs.Conclusion: The findings indicate the immediate need to enhance health education among the RDW about newborn danger signs before their hospital discharge. Community based health education programs can be a cost effective intervention to increase awareness and early recognition of neonatal danger signs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".