Assessing the Contextual Effect of Community in the Utilization of Postnatal Care Services in Ghana
Bibliographic record
Abstract
Abstract BackgroundInequalities in the use of postnatal care services (PNC) in Ghana have been linked to poor maternal and neonatal health outcomes. This has ignited a genuine concern that PNC interventions with a focus on influencing solely individual-level risk factors do not achieve the desired results. This study aimed to examine the community-level effect on the utilization of postnatal care services. Specifically, the research explored clusters of non-utilization of PNC services as well as the effect of community-level factors on the utilization of PNC services, with the aim of informing equity-oriented policies and initiatives.MethodsThe 2014 Ghana Demographic and Health Survey GDHS dataset was used in this study. Two statistical methods were used to analyze the data; spatial scan statistics were used to identify hotspots of non-use of PNC services and second two-level mixed logistic regression modeling was used to determine community-level factors associated with PNC services usage. ResultsThis study found non-use of PNC services to be especially concentrated among communities in the Northern region of Ghana. Also, the analyses revealed that community poverty level, as well as community secondary or higher education level, were significantly associated with the utilization of PNC services, independent of individual-level factors. In fact, this study identified that a woman dwelling in a community with a higher concentration of poor women is less likely to utilize of PNC services than those living in communities with a lower concentration of poor women (Adjusted odds ratio (AOR) = 0.60, 95%CI: 0.44 - 0.81). Finally, 24.0% of the heterogeneity in PNC services utilization was attributable to unobserved community variability.ConclusionThe findings of this study indicate that community-level factors have an influence on women’s health-seeking behavior. Community-level factors should be taken into consideration for planning and resource allocation purposes to reduce maternal health inequities. Also, high-risk communities of non-use of obstetric services were identified in this study which highlights the need to formulate community-specific strategies that can substantially shift post-natal use in a direction leading to universal coverage.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".