Neonatal care practice and factors affecting in Southwest Ethiopia: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: A significant proportion of neonatal mortality can be prevented by the provision of the minimum neonatal care package. However, about 3 million neonates die each year globally because of lack of appropriate care. This situation is the worst in Ethiopia. Thus, the objective of this study was to determine the status of neonatal care and identify factors affecting. METHODS: A mixed methods study involving both quantitative and qualitative methods was conducted from September 2012-December 2013 in Southwest Ethiopia. Randomly selected sample of 3463 mothers were interviewed to collect the quantitative data. Twelve in-depth interviews with purposively selected key informants and six focus-group discussions with purposively selected mothers were conducted for the qualitative data. Mixed-effects multilevel linear regression model was used to identify predictors of neonatal care practice by using STATA 13. Audio recording, transcription and thematic content analysis was done for the qualitative data. RESULTS: The overall status of neonatal care practice was 59.5 % (95 % CI: 57.6 %, 61.3 %). Of the respondents, 53.8 % received tetanus toxoid, 23.8 % planed for birth, 41.9 % received at least one antenatal care and 43.0 % received adequate information during pregnancy. Only, 17.5 % received skilled care at birth and 95.0 % received social support. Of the neonates, 96.5 % received appropriate thermal care, 86.5 % received clean cord care, 64.1 % initiated breast-feeding within one hour, 91.5 % were on exclusive breast-feeding, 56.5 % received appropriate bathing and 8.1 % received vaccination on date of birth. Place of residence, maternal education, husband's occupation, wealth quintiles, birth order and inter-birth interval were identified as predictors of neonatal care practice. CONCLUSIONS: The status of neonatal care practice was low in the study area. Skilled care at birth and receiving vaccination on date of birth were the worst practices. Factors affecting neonatal care existed both at cluster level and at the individual level and included socio demographic, economic and obstetric factors. Appropriate birth spacing, birth limiting and behaviour change communications on the importance of neonatal care are recommended.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".