Beyond Medically Complex Pregnancy: A Scoping Review to Understand How Complexity in Pregnancy is Conceptualized
Bibliographic record
Abstract
BACKGROUND: The goal of this scoping review was to better understand how complexity in pregnancy is conceptualized. Specific objectives were to (1) identify factors that are conceptualized in the literature as complicating or impacting pregnancy; and (2) summarize tools and programs that have been implemented to support pregnant people with complex care needs. METHODS: Electronic databases were searched from January 2000 to July 2020 and supplemented by bibliographic searches and citation chaining, to identify articles that described at least one nonmedical and one medical risk factor during pregnancy. We focused on complexity prior to the onset of labor and only included primary studies conducted in middle- or high-income countries. More than 6000 records were screened independently by 3 reviewers at the abstract and title level. RESULTS: Fourteen articles met inclusion criteria. Eight studies described antenatal risk scoring systems, including the Florida Healthy Start Prenatal Risk Screen, the Kindex risk screening tool, the prenatal event history calendar, and the Rotterdam Reproductive Risk Reduction score card. We abstracted 85 medical factors and 25 nonmedical factors from the literature. Nonmedical factors that were conceptualized as complicating pregnancy or birth could be grouped into 4 domains: characteristics of the childbearing person (7 factors), socioeconomic conditions (7 factors), family and social life (5 factors), and psychoemotional health (6 factors). DISCUSSION: We found limited scholarly research and few assessment tools that broaden the discussion of complexity in pregnancy beyond medical multimorbidity. Multiple dimensions of health should be integrated into a complexity framework for pregnancy that account for the diverse contexts and needs of pregnant people. An important part of this process is the development of a shared language to describe complexity that is strength based and acknowledges how environments, health care encounters, and the larger sociocultural context can affect pregnant people's medical status in pregnancy.
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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 | 0.026 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| 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".