Determinants of late and/or inadequate use of prenatal healthcare in high-income countries: a systematic review
Bibliographic record
Abstract
BACKGROUND: Prenatal healthcare is likely to prevent adverse outcomes, but an adequate review of utilization and its determinants is lacking. OBJECTIVE: To review systematically the evidence for the determinants of prenatal healthcare utilization in high-income countries. METHOD: Search of publications in EMBASE, CINAHL and PubMed (1992-2010). Studies that attempted to study determinants of prenatal healthcare utilization in high-income countries were included. Two reviewers independently assessed the eligibility and methodological quality of the studies. Only high-quality studies were included. Data on inadequate use (i.e. late initiation, low-use, inadequate use or non-use) were categorized as individual, contextual and health behaviour-related determinants. Due to the heterogeneity of the studies, a quantitative meta-analysis was not possible. RESULTS: Ultimately eight high-quality studies were included. Low maternal age, low educational level, non-marital status, ethnic minority, planned pattern of prenatal care, hospital type, unplanned place of delivery, uninsured status, high parity, no previous premature birth and late recognition of pregnancy were identified as individual determinants of inadequate use. Contextual determinants included living in distressed neighbourhoods. Living in neighbourhoods with higher rates of unemployment, single parent families, medium-average family incomes, low-educated residents, and women reporting Canadian Aboriginal status were associated with inadequate use or entering care after 6 months. Regarding health behaviour, inadequate use was more likely among women who smoked during pregnancy. CONCLUSION: Evidence for determinants of prenatal care utilization is limited. More studies are needed to ensure adequate prenatal care for pregnant women at risk.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".