Acceptability of digital health intervention during pregnancy to inform women about postpartum contraception (DIGICAP): a pilot randomised controlled study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it