Predictors of knowledge and practice of exclusive breastfeeding among health workers in Mwanza city, northwest Tanzania
Bibliographic record
Abstract
Universal exclusive breastfeeding (EBF) for the first 6 months is estimated to reduce infant mortality by 13–15% (9 million) in resource poor countries. Although 97% of women initiate breastfeeding in Tanzania, exclusive breastfeeding for 6 months remains below 50%. Accurate knowledge and practical skills pertaining to exclusive breastfeeding among health workers is likely to improve breastfeeding rates. Our study reports the health workers’ knowledge and practice on EBF in Mwanza City, northwest of Tanzania. One principal researcher and two research assistants conducted data collection from 11 June–6 July 2012. In total, 220 health care workers including: 64 clinicians (medical specialists, residents, registrars, assistant medical officers and clinical officers) and 156 nurses were interviewed using a structured knowledge questionnaire. Amongst 220 health workers, 106 were observed supporting Breastfeeding using a checklist. Logistic regression was used to determine factors associated with exclusive breastfeeding knowledge and desirable skills. Almost half of the 220 health workers interviewed correctly described EBF as defined by the World Health Organization. Only 52 of 220 respondents had good knowledge. In the adjusted analysis, working at hospital facility level compared to dispensary (OR 2.1; 95% CI 1.1–4.0, p -value = 0.032) and attending on job training (OR 2.7; 95% CI 1.2–6.1, p -value = 0.015) were associated with better knowledge. In total, 38% of respondents had a desirable level of practical skills. Clinicians were more likely to have good practice (OR 3.6; 95% CI 1.2–10.8; p -value = 0.020) than nurses. Most of the health workers had no training on EBF, and were not familiar with breastfeeding policy. Less than 25% of healthcare workers surveyed had good knowledge of EBF. These findings identify the need for comprehensive training and mentoring of health workers on exclusive breastfeeding, making breastfeeding policies available and understood, along with supportive supervision and monitoring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".