Development and Validation of a National Data Registry for Midwife‐Led Births: The Midwives Alliance of North America Statistics Project 2.0 Dataset
Bibliographic record
Abstract
INTRODUCTION: In 2004, the Midwives Alliance of North America's (MANA's) Division of Research developed a Web-based data collection system to gather information on the practices and outcomes associated with midwife-led births in the United States. This system, called the MANA Statistics Project (MANA Stats), grew out of a widely acknowledged need for more reliable data on outcomes by intended place of birth. This article describes the history and development of the MANA Stats birth registry and provides an analysis of the 2.0 dataset's content, strengths, and limitations. METHODS: Data collection and review procedures for the MANA Stats 2.0 dataset are described, along with methods for the assessment of data accuracy. We calculated descriptive statistics for client demographics and contributing midwife credentials, and assessed the quality of data by calculating point estimates, 95% confidence intervals, and kappa statistics for key outcomes on pre- and postreview samples of records. RESULTS: The MANA Stats 2.0 dataset (2004-2009) contains 24,848 courses of care, 20,893 of which are for women who planned a home or birth center birth at the onset of labor. The majority of these records were planned home births (81%). Births were attended primarily by certified professional midwives (73%), and clients were largely white (92%), married (87%), and college-educated (49%). Data quality analyses of 9932 records revealed no differences between pre- and postreviewed samples for 7 key benchmarking variables (kappa, 0.98-1.00). DISCUSSION: The MANA Stats 2.0 data were accurately entered by participants; any errors in this dataset are likely random and not systematic. The primary limitation of the 2.0 dataset is that the sample was captured through voluntary participation; thus, it may not accurately reflect population-based outcomes. The dataset's primary strength is that it will allow for the examination of research questions on normal physiologic birth and midwife-led birth outcomes by intended place of birth.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".