Health Research Using Facebook to Identify and Recruit Pregnant Women Who Use Electronic Cigarettes: Internet-Based Nonrandomized Pilot Study
Bibliographic record
Abstract
BACKGROUND: Participant recruitment is often a challenge, particularly enrolling individuals with relatively rare characteristics. The wide reach of social media may provide a mechanism to overcome these challenges. OBJECTIVE: This paper aimed to provide information to researchers who seek to recruit participants from rare populations using social media for studies with demanding protocols. We aimed to describe a pilot study protocol that identified and enrolled pregnant women (second or third trimester) who were exclusive users of electronic cigarettes (e-cigarettes). We have described the recruitment methods, time, and cost; examined advertisement types that were more or less successful; discussed participant retention and relationship management; and described the process of collecting biological data. METHODS: In an open-access, nonrandomized pilot study, we placed Facebook advertisements that were selectively targeting women who were likely to be pregnant and interested in e-cigarettes or vaping. The advertisements invited individuals to complete a fully automated eligibility screener based on Qualtrics. Eligible participants were asked to (1) complete a Web-based survey that collected detailed information on the use of e-cigarettes, including the exact type of device and electronic liquid, (2) report the frequency and intensity of e-cigarette use for 3 months before pregnancy and during each trimester, and (3) provide a saliva specimen for a nicotine biomarker assay. We collected a photograph of each participant's e-cigarette device, 8 weeks after the mother's due date, to allow corroboration of the self-report and the baby's birth weight and gestational age from the participant's physician. RESULTS: Participants were recruited between August 19 and October 26, 2017. We enrolled 20 participants in 2 months at a cost of US $3421.28. Baseline data were collected for all 20 participants. Of the 20 women enrolled, 16 provided a saliva sample, 4 provided a photo of the e-cigarette device, and 10 provided physician contact information. Of the 10 physicians contacted by mail, 6 responded with information on the participants and their babies. CONCLUSIONS: Study findings suggest that Facebook's targeting criteria should focus on e-cigarette users to maximize advertisement exposure of potentially eligible women. In addition, saliva sample collection was feasible among pregnant women (second or third trimester) who were exclusive e-cigarette users, but obtaining photographs and physician reports was problematic and called for further refinement. These lessons are likely useful to others who are seeking to use social media to recruit participants from rare populations into studies with demanding protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".