Acceptability of digital health intervention during pregnancy to inform women about postpartum contraception (DIGICAP): a pilot randomised controlled study
Bibliographic record
Abstract
BACKGROUND: Pregnancy within a year of childbirth has negative impacts on women and their children's health. We developed a digital health intervention (DHI) to empower women in contraceptive choices postpartum. Our pilot randomised controlled trial (RCT) aimed to establish the feasibility of a main RCT of the effects of the DHI compared with standard care on long-acting contraception use. METHODS: Our pilot RCT recruited 52, 20-24 weeks pregnant women in NHS Lothian, UK between October 2022 and April 2023. Participants were randomised 7:3 to receive either the DHI (n=37) in addition to standard care, or standard care alone (n=15). Telephone survey follow-up was at 24 weeks' gestation and 6 weeks postpartum. Semi-structured qualitative interviews (n=10) were conducted with participants receiving the DHI. RESULTS: All eligible women joined the study and completed follow-up. All intervention participants found the animation highly acceptable; one participant requested text message discontinuation. We completed followed up on 37/37 (100%) of participants. DHI participants reported they valued access to credible contraceptive information that supported decision making in a non-pressurised way. CONCLUSIONS: Our DHI is highly acceptable and a trial is feasible. A larger trial is needed to establish if the DHI increases uptake of long-acting reversible contraception postpartum and reduces unintended pregnancies within 12 months of childbirth. TRIAL REGISTRATION NUMBER: (Trial registration ISRCTN48521918).
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".