Adapting to the Pandemic: Protocol of a Web-Based Perinatal Health Study to Improve Maternal and Infant Outcomes
Bibliographic record
Abstract
BACKGROUND: The identification of interconnected health risks during the perinatal period offers an opportunity to prevent negative maternal and infant health outcomes. Marijuana, opioid, and other substance use during pregnancy is a rapidly growing public health concern with significant and costly health consequences for the woman and the developing fetus. Pregnant persons who misuse substances are disproportionately more likely to engage in risky sexual behaviors resulting in sexually transmitted infections (STIs), which are on the rise in this population and can lead to adverse effects on maternal health and on fetal development. OBJECTIVE: Our goal is to continue testing an innovative and low-cost technology-delivered intervention, the Health Check-Up for Expectant Moms (HCEM), which simultaneously targets alcohol and drug use and STI risk during pregnancy, both of which are on the rise during the COVID-19 pandemic. METHODS: We describe the ways in which we have adapted the web-based HCEM intervention to continue recruitment and study enrollment during the pandemic. RESULTS: Study recruitment, visits, and participant safety assessments were all successfully modified during the initial year of the COVID-19 pandemic. Compared to in-person recruitment that occurred prepandemic, remote recruitment yielded a greater proportion of women enrolled in the study (83/136, 61.0% vs 43/52, 83%) in a shorter period (12 months vs 7 months). CONCLUSIONS: Despite study challenges related to the pandemic, including time and effort adapting to a remote protocol, remote recruitment and visits for this study were found to constitute a successful approach. TRIAL REGISTRATION: ClinicalTrials.gov NCT03826342; https://clinicaltrials.gov/ct2/show/NCT03826342. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30367.
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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 | 0.051 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.013 |
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".