Risk Identification in Perinatal Health Care Settings via Technology-Based Recruitment Methods: Comparative Study
Bibliographic record
Abstract
Background Digital screening and intervention tools have shown promise in the identification and reduction of substance use in health care settings. However, research in this area is impeded by challenges in integrating recruitment efforts into ongoing clinical workflows or staffing multiple study clinics with full-time research assistants, as well as by the underreporting of substance use. Objective The aim of the study is to evaluate pragmatic methods for facilitating study recruitment in health care settings by examining recruitment rates and participant characteristics using in-person–based versus flyer approaches. Methods This study compared recruitment rates at a Women’s Health clinic in the Midwest under 2 different recruitment strategies: in person versus via a flyer with a QR code. We also examined the disclosure of substance use and risk screener positivity for the 2 strategies. We also obtained information about the current use of technology and willingness to use it for study participation. Results A greater percentage of patients recruited in person participated than those recruited via flyers (57/63, 91% vs 64/377, 17%). However, the final number recruited in each group was roughly equal (n=57 vs n=64). Additionally, participants recruited via flyers were more likely to screen positive for alcohol use risk on the Tolerance, Annoyed, Cut Down, Eye-Opener alcohol screen than those recruited at the clinic (24/64, 38% vs 11/57, 19%; χ21=4.9; P=.03). Participants recruited via flyers were also more likely to screen positive for drug use risk on the Wayne Indirect Drug Use Screener than those recruited at the clinic (20/64, 31% vs 9/57, 16%; χ21=4.0; P=.05). Furthermore, of the 121 pregnant women, 117 (96.7%) reported owning a smartphone, 111 (91.7%) had an SMS text message plan on their phone, and 94 (77.7%) reported being willing to receive SMS text messages or participate in a study if sent a link to their phone. Conclusions The distribution of flyers with a QR code by medical staff appears to be an efficient and cost-effective method of recruitment that also facilitates disclosure while reducing the impact on clinic workflows. This method of recruitment can be useful for data collection at multiple locations and lead to larger samples across and between health systems. Participant recruitment via technology in perinatal health care appears to facilitate disclosure, particularly when participants can learn about the research and complete screening using their own device at a place and time convenient for them. Pregnant women in an urban Midwestern hospital had access to and were comfortable using technology.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".