Using data to explore vulnerable women's utilization of maternity health care
Bibliographic record
Abstract
IntroductionInequitable access to appropriate maternity health care is an issue for vulnerable women that negatively impacts health outcomes. As part of a feasibility study on midwifery services for vulnerable women, we used administrative data to further our understanding of socially disadvantaged women’s use of the primary care system during pregnancy.
 Objectives and ApproachTo better understand maternity health service utilization and social vulnerability of women in Calgary Alberta, a research partnership was formed between Alberta Health Services and a social service agency that serves clients experiencing, poverty, and food insecurity and were at risk for homelessness. This multi-phase study linked postal code data to data from provincial databases. Variables included socioeconomic characteristics, prenatal health care utilization and maternal and birth outcomes for the years 2013 to 2015.
 ResultsDatabases accessed included the Alberta Perinatal Health Program (APHP), Alberta Health Practitioner Claims Database, AHS Admission Discharge Transfer Database, Discharge Abstracts Database, National Ambulatory Care Reporting, and Provincial Registry Database.
 Data linkages yielded a total sample size of 7493 women, with 15.5% of women qualifying as ‘socially vulnerable’. Women receiving social assistance are relatively younger, experience more pregnancies, have higher antenatal risk scores and accessed maternal and emergency care more often and later in their pregnancy than those women who are not accessing social services. Our results suggest women living in vulnerable circumstances experience higher risk pregnancies that those not living in vulnerable circumstances. Therefore a maternity care model such as midwifery, which uses a holistic approach to care may be beneficial for vulnerable women.
 Conclusion/ImplicationsFindings from our study confirm that women experiencing poor social circumstances are at increased risk for complications during pregnancy and birth. Therefore, we need to further investigate utilizing maternity models of care that serve both the maternal health needs and the social needs of this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".