Knowledge regarding reproductive and child health: an intervention study among ASHAs in a block of West Bengal
Bibliographic record
Abstract
BACKGROUND ASHAs are the first port of call for any health related demands of the local community, especially women and childrenwho find it difficult to access health services; hence it is imperative to assess their knowledge regarding reproductive and child health (RCH) and factors influencing their knowledge. \nMETHODS A cross-sectional study (Pre/Post-test study design) was conducted on ASHAs working in Singur block of West Bengal to determine their knowledge regarding specific pre-defined topics related to RCH services, to assess the effect of health education on their baseline knowledge and to study the association between their level of education and knowledge regarding RCH services. \nRESULTS The mean age of the sample was 34.42 years (S.D: ±2.91). About \n27%, 35% and 39% of the ASHAs have completed secondary, higher \nsecondary and graduate level of education, respectively. The mean baseline \nscore was 9.65 (maximum-27) which improved to 21.21 following health education and this difference was statistically significant (p<0.001). A statistically significant difference in pre-test (p=003) and post-test scores (p=0.012) was observed between ASHAs with secondary and graduate level of education. \nCONCLUSION There is deficiency in knowledge of ASHAs regarding various aspects of RCH services. Their education level has a strong positive influence not only on their knowledge levels but also on their learning ability following training sessions. The study recommends high quality refresher training at periodic intervals and strengthening of existent training programs for ASHAs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".