Digital Health Tools for Miscarriage Support: A Survey of Canadian Women Facing Early Pregnancy Loss
Bibliographic record
Abstract
OBJECTIVES: Early pregnancy loss (EPL) affects 1 in 4 recognised pregnancies, yet often lacks patient-centred supportive care. This study assesses the feasibility and acceptance of a digital health tool to support those affected by EPL. The objectives are to (1) understand the experiences of those who have miscarried, (2) explore their methods of accessing health information, and (3) determine their preferences regarding digital tool content and design. METHODS: weeks gestation in the preceding 2 years. Recruitment was via social media and hospital posters. Participants completed an online survey and optional follow-up interview between September 2021 and April 2022. Survey responses were analysed using descriptive statistics. Interview findings are presented in a separate paper. Local ethics approval was obtained. RESULTS: Of the 185 survey respondents, 28% revealed that they are somewhat or very dissatisfied with the overall health care they received for their miscarriage. Thirty-nine percent of survey respondents are somewhat or very dissatisfied with how their mental/emotional health was addressed. Notably, 82% supported the development of a digital health tool for EPL care. Ninety-one percent of survey respondents use the internet to access health information. CONCLUSIONS: Many participants reported dissatisfaction with their care after EPL but showed strong interest in a user-friendly digital tool that provides general information and mental health support. These findings, along with qualitative interview data, will guide the development and testing of the desired digital health tool, aiming to enhance patient experience and support after miscarriage.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".