Using Intervention Mapping to co-develop and pilot Orchid: a new digital tool for reproductive life planning. (Preprint)
Bibliographic record
Abstract
Abstract Background Most people make no health or lifestyle changes before pregnancy, missing a key opportunity to improve outcomes. Consequently, nearly half of UK pregnancies are unplanned, disproportionately affecting underserved groups and widening health inequalities. Digital health interventions (DHIs) offer promise but require systematic, theory-driven development to ensure effectiveness and real-world applicability. Objective This study aimed to use Intervention Mapping to codevelop and pilot Orchid, a novel DHI designed to support people of reproductive age to understand their pregnancy preferences and develop a reproductive life plan (RLP). Methods We used Intervention Mapping steps 1‐4 to guide the systematic, theory-informed codevelopment of Orchid. A multidisciplinary planning group and codevelopment group of 21 members of the public contributed throughout. At step 1, previous research and a scoping review of existing RLPs informed the program goals and logic model of the problem. During step 2, we identified performance objectives and behavioral determinants to specify practical strategies for each target behavior. At step 3, we applied the Capability, Opportunity, Motivation–Behavior (COM-B) model and relevant behavior change techniques to guide intervention design. Finally, at step 4, Orchid was co-designed as a website and mobile app providing users with a pregnancy preference group and prediction of pregnancy, a dynamic RLP, tailored evidence-based information, and optional goal-setting features to support behavior change. Orchid was piloted between January and May 2025 to explore its feasibility and acceptability in health care settings. Interviews with users, nonusers, and health care professionals were conducted and quantitative data from users were collected. Results These findings indicate that implementation was feasible, and health care professionals found it acceptable to recommend Orchid to patients, though noted barriers including time constraints and competing priorities. Overall, 153 people signed up to Orchid; 68% (72/106) of eligible users received a pregnancy preference group and 27% (32/119) of eligible users completed a full RLP. Users were positive about Orchid, appreciating its content and design, noting that Orchid contained a wealth of information about reproductive health presented in an easy-to-understand manner. They valued the autonomy, convenience, and privacy afforded by the digital format, and found it acceptable to be recommended Orchid within a health care setting. Orchid uptake was lower than anticipated, and use was limited; this was partly expected given the short pilot period, but feedback also suggested targeted recruitment and navigation improvements could enhance uptake and engagement. Conclusions Orchid is the first co-designed DHI to support reproductive health across the life course. Its systematic development, theoretical foundation, strong user involvement, and positive pilot testing position it as a promising, scalable innovation to support reproductive health, deliver credible information in accessible formats, and promote preventative, community-based care across the National Health Service.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".