Interest in digital health tools for miscarriage support: A qualitative assessment of Canadian women facing early pregnancy loss
Bibliographic record
Abstract
BACKGROUND: Early pregnancy loss (EPL) occurs in 10%-15% of all pregnancies but remains an underrecognized and undertreated condition. In Canada, resources to support individuals and their partners facing EPL remain scarce despite a high burden of psychosocial sequelae. Digital health tools hold the potential to fill important gaps in reproductive healthcare. OBJECTIVES: We sought to better understand the perspectives of individuals who experienced pregnancy loss and explore how digital health tools could offer support. DESIGN: We conducted a qualitative study with grounded theory methodology to address our objectives. METHODS: The study was conducted between September 2021 and April 2022 in Ottawa, Canada. Participants between 18 and 45 years of age who resided in Canada and experienced EPL up to 12 + 6 week gestation within the last 2 years were included. Enrolled participants who provided informed consent completed a single in-depth interview. Data were analyzed iteratively by two trained research team members with thematic techniques supported by NVivo software. RESULTS: = 10) between 31 and 40. Qualitative analysis identified three primary themes centered around participants' experiences of miscarriage, access to information and support for EPL in Canada, and desires and preferences for a digital miscarriage tool. CONCLUSION: Miscarriage is an emotionally difficult experience for women and their loved ones, who often do not receive timely and compassionate care within the healthcare system. Participants were highly motivated to co-develop a digital intervention for EPL that is designed to fill gaps in care. The digital companion would assist individuals through their miscarriage journey by providing evidence-based and locally relevant medical information as well as avenues to access both professional and informal forms of psychosocial support.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".