Remote recruitment for Essential Coaching for Every Mother during COVID-19
Bibliographic record
Abstract
Abstract Background With a decrease in in-person support and increase in perinatal mental health concerns during the coronavirus pandemic, innovative strategies, such as mHealth, are more important than ever. However, due to physical distancing recommendations, recruitments for perinatal research needs to shift. The objective of this study is to desire the process evaluation of recruitment and retention of women for an mHealth pre-post intervention study for Essential Coaching for Every Mother . Methods Three methods were used for recruitment: social media, posters in hospital, and media outreach. First time mothers were eligible for enrollment antenatally (37+ weeks) and postnatally (<3 weeks). Eligibility screening occurred remotely via text message. Outcomes were days to recruit 75 participants, eligibility vs. ineligibility rates, dropout and exclusion reasons, survey completion rates, perinatal timing of enrollment, and recruitment sources. Results Recruitment ran July 15 th -September 19 th (67 days) with 200 screened and 88 enrolled, 70% antenatally. It took 50 days to enroll 75 participants. Mothers recruited antenatally (n=53) were more likely to receive all intervention message (68% vs. 19%). Mothers recruited postnatally (n=35) missed more messages on average (13.8 vs. 6.4). Participants heard about the study through family/friends (31%), news (20%), Facebook groups (16%), Facebook ads (14%), posters (12%), or other ways (7%). Conclusion Antenatal recruitment resulted in participants enrolling earlier and receiving more of the study messages. Word of mouth and media outreach were successful, followed by advertisement on Facebook. Remote recruitment was a feasible way to recruit for Essential Coaching for Every Mother .
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".