Socio-Economic Factors and Knowledge Influencing Newborn Care Practices: Experience at Dhaka Shishu Hospital
Bibliographic record
Abstract
Reducing maternal and neonatal mortality remains a big challenge for a developing country likeBangladesh. Mothers’ knowledge in neonatal care plays an important role in bringing down themortality as well as morbidity. This study was conducted in Dhaka Shishu Hospital during theperiod of December 2007 to February 2008 and was based on primary data collected on socioeconomicstatus, knowledge and practice of mothers of neonates attending the hospital. A total of 400 motherswere interviewed. More than fifty percent mothers had an appropriate knowledge on feeding neonates,hand washing before handling neonates, care of eye, care of umbilicus and they were practicing aswell. Where as less than fifty percent mothers had appropriate knowledge on keeping neonateswarm, cutting hair, bathing, vaccination, oil massage and their practice rate also commensuratewell with their knowledge level. Majority of the mothers were in the age group of 21-25 years,having completed primary education or passed SSC exam. They were house wives living in an urbanarea, with a monthly family income of 3000-7000 taka. Statistically significant association wasfound between socio demographic variables and knowledge and practices on neonatal care of themothers.Ibrahim Med. Coll. J. 2010; 4(1): 17-20Key words: Socio-economic factors; knowledge and practices; neonatal careDOI: 10.3329/imcj.v4i1.5930
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".