Challenges and Opportunities in Recruiting a Very Large Sample of Pregnant Individuals: Secondary Analysis of an Online Nationwide Randomized Controlled Trial
Bibliographic record
Abstract
Background: It is challenging to recruit vulnerable populations such as pregnant individuals, particularly during the perinatal period, which involves significant life changes and stressful situations that may create barriers to participation. Barriers to participation are even more prominent in historically marginalized populations, such as minoritized and low-income populations. Current literature is limited on recruitment methods, and specific activities may be best to recruit diverse pregnant individuals into online studies for the promotion of perinatal mental health. Objective: The aim of this paper is to describe recruitment methods and strategies used to recruit a large sample (n=1953) of diverse pregnant individuals to an online nationwide large-scale randomized controlled trial, the Mamma Mia Study. Methods: The Mamma Mia study is a multisite randomized controlled trial of an online- and mobile-based intervention for preventing and reducing perinatal depressive symptoms, based in the United States. The study intended to ensure a diverse national representation in the study population, with internal team demographic goals of at least 50% of participants identifying as a race or ethnicity other than White and at least 25% low-income (defined as a household income of less than US $50,000). Institutional review board-approved active and passive recruitment methods, both online nationally and in-person locally, were used to recruit eligible pregnant individuals. Results: Combining local and in-person with national and online recruitment methods allowed for successful recruitment of a large and diverse sample of pregnant individuals, despite the necessity for several pivots due to national events (eg, the COVID-19 pandemic). In addition, this layered approach allowed the study to continue during an unplanned world event and be responsive to pivoting to meet recruitment goals. Recruitment approaches and methods that were the most successful were establishing community partnerships both online nationally and in-person locally, dedicated research time to focus on recruiting historically marginalized groups for a more representative sample, allocation of study time and resources to recruitment preparation, and dedicated internal research team recruitment planning and tracking. Conclusions: Researchers should continue to publish and disseminate specific details about recruitment efforts and results, highlighting both aspects of success and lessons learned, as well as the pivot points in their recruitment methods for shared learning.
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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 | 0.311 | 0.343 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".