Late initiation and low utilization of postnatal care services among women in the rural setting in Northwest Tanzania: a community-based study using a mixed method approach
Bibliographic record
Abstract
BACKGROUND: Maternal and newborn mortality is high immediately after childbirth and up to 42 days postnatally despite the availability of interventions. Postnatal care is crucial in preventing mortality and improving the health of women and newborns. This prospective cohort study investigated the initiation and utilization of postnatal care at health facilities and explored users' and providers' perspectives on utilization of postnatal care services. METHODS: A sequential explanatory mixed method was used involving women who were followed from the 3rd trimester of pregnancy to 3-4 months postnatally in Northwest, Tanzania. From January to December 2018, a door-to-door survey was conducted 3-4 months postnatally among 1385 of these women. A convenience sample of women and community health workers participated in focus group discussions, and traditional birth attendants and nurses participated in key informant interviews to complement quantitative data. Data analyses were conducted using STATA version 13 and NVIVO version 12. STUDY FINDINGS: Approximately, one half of participants attended postnatal care within 42 days after delivery. Postnatal care seeking within 48 h after delivery was reported by 14.6 % of the participants. Women who attended antenatal care at least four times, delivered at health facilities or experienced delivery-related complications were more likely to seek postnatal care. Limited knowledge on the postnatal care services and obstetric complications after childbirth, and not being scheduled for postnatal care by health providers negatively influenced services uptake. Overwhelming workload and shortages of supplies were reported to hinder the provision of postnatal care services. CONCLUSIONS: Utilization of postnatal care services remains low in this setting as a result of a number of disparate and complex factors that influence women's choices. Provision of effective postnatal care is hindered by lack of supplies, staffing, and inadequate infrastructure. To ensure accessibility and availability of quality services in this setting, both demand and supply sides factors need to be addressed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".