Assessment of Effective Coverage of Antenatal Care and Associated Factors in Squatter Settlements of Islamabad Capital Territory, Pakistan: An Analytical Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Effective coverage of antenatal care (ANC) goes beyond contact coverage and assesses the quality of service provided. We used World Health Organization's recommended positive pregnancy guidelines to assess effective coverage and factors associated with the utilization of ANC among women in squatter settlements of Islamabad Capital Territory. METHODS: We conducted a household survey in the study area with 416 women who had given birth in the past one year. Face-to-face interviews were conducted after the selection of study subjects was done through a systematic random sampling approach. Statistical analysis was carried out using Statistical Package for the Social Sciences 22 (SPSS 22; IBM corp. Armonk, NY). Effective ANC coverage was defined as four or more ANC visits along with all WHO-recommended interventions received at least once during ANC. Adjusted odds ratios (adjOR) with 95% CI were calculated using binary logistic regression to determine the independent effects of all associated factors on the outcome. RESULTS: Of the 416 women interviewed, 399 (95.6%) had availed ANC services at least once. The coverage of 4+ ANC visits was 92% but effective coverage was only received by 35% women. The proportion of women who received nutritional interventions, maternal and fetal assessment and other preventive measures was 68%, 51% and 80.8% respectively. Maternal education (adjOR, 95% CI = 4.8[2.4-9.3]), family income (2.3[1.1-5.1]), multiparity (1.7[1.1-2.9]), place of first ANC visit (4.2[1.7-10.5]) and distance from a health facility (2.2[1.3-3.6]) were independently associated with the non-utilization of effective ANC. CONCLUSION: Despite a very high crude coverage of ANC services, the study shows a very low proportion of women receiving effective coverage. This stresses the importance of measuring the proportion of the population that receives health services with quality to monitor progress toward achieving universal health 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".