Determinants of Frequency and Contents of Postnatal Care Among Women in Ezha District, Southern Ethiopia, 2020: Based on WHO Recommendation
Bibliographic record
Abstract
BACKGROUND: Postnatal care is a constellation of preventive care, practices, and assessments intended to detect and treat complications for both the mother and the newborn in the first six following birth. Monitoring of the content and frequency of the PNC is required to make the service provision more successful. However, several studies centered on the general PNC visits, and pieces of evidence were limited at the country level on the core content of the PNC, including the current study area. Therefore, this study aimed to identify determinants of the frequency and content of PNC visits among women who gave birth in the last 12 months in Ezha district, Southern Ethiopia. METHODS AND MATERIALS: A community-based cross-sectional survey was conducted in the Ezha district to collect data from 568 respondents by using pre-tested, interviewer-administered questionnaires. Data were entered into EpiData3.1 and exported to SPSS version 23 for analysis. To determine the wealth status of the respondents, the Principal Component Analysis was undertaken. To evaluate the determinants of frequency and the content of PNC, both binary logistic regression and generalized linear regression with Poisson type were applied respectively. RESULTS: Nearly a quarter (23.9%) of respondents received three or more postnatal visits, and only 81 (14.6%) respondents received all the PNC service contents suggested by WHO. Identified predictors of the core content of PNC were, frequencies of ANC (AOR: 1.25, 95% CI: 1.15-1.35), enrollment in community-based health insurance scheme (AOR: 0.69 (95% CI: 0.64-0.75), and PNC frequency (AOR: 0.64, (95% CI (0.57-0.73). CONCLUSION: A low level of WHO-recommended frequency and content of the PNC were identified in the study area. To achieve better utilization, strengthening efforts to improve adequate ANC uptake, enrollment in the CBHI scheme, and working on a model household creation were, therefore, should be crucial measures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".