Digital Health Needs of Women With Postpartum Depression: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Although approximately 10% of new mothers in Canada develop postpartum depression (PPD), they face many barriers when accessing care. eHealth offers a unique opportunity to provide psychosocial skills and support to new mothers; however, patient populations are not consistently engaged in eHealth development processes. Thus, the diversity of women's backgrounds and needs are often not reflected in existing tools. OBJECTIVE: This study aims to engage women from a variety of backgrounds and locations around British Columbia (BC) who have previously experienced PPD to determine the unmet psychoeducational needs of women with PPD and how a web-enabled platform used to deliver psychosocial skills and education to assist in the management of PPD could fulfill those needs. METHODS: Focus groups were conducted in 7 cities across BC with a total of 31 women (mean age 34.5 years, SD 4.9), with each group ranging from 2-7 participants. Focus groups were cofacilitated by the study coordinator and a local service provider in each community using a semistructured guide to discuss participants' needs, ideas, and opinions as they relate to the use of technology in PPD management. Transcripts were approached inductively using thematic analysis to identify themes and qualitative description to frame what was observed in the data. RESULTS: A total of 5 themes were identified: bridging gaps to meet needs; providing validation to combat stigma; nurturing capacity to cope, manage, and/or reach wellness; empowering people to take ownership over their mental health; and offering customization to ensure relevance. Each theme identified a need (eg, combatting stigma) and a way to address that need using a web-enabled intervention (eg, providing validation). At the intersection of these themes was the overarching value of promoting agency for women experiencing PPD. CONCLUSIONS: Ultimately, new mothers require accessible mental health care that promotes their agency in mental health care decision making. Our participants believed that a web-enabled intervention could help meet this need. These data will be used to guide the design of such an intervention, with the eventual implementation of this resource as a first-line management option for PPD.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".