Digital Tools Fill a Gap in Mental Health Screening and Support, Particularly for Women Lacking Strong Social Networks
Bibliographic record
Abstract
Background Roughly 11% of women suffer from postpartum depression nationwide; however, many believe the condition to be widely underreported, in part due to inadequate screening and stigma associated with the condition. Social support networks can help to prevent or mitigate symptoms related to postpartum depression. Single mothers tend to suffer from this condition at a higher rate than married women as they tend to have weaker social networks compared to married women. Objective The primary objective ws to determine whether gaps exist in mental health screening and whether digital screening tools can help to fill these gaps. The secondary objective ws to determine whether digitally delivered support proves to be more or less beneficial to subsets of women, namely based on their marital status. Methods A survey about mental health history, support, experience with mental health screeners, and characteristics of social networks was sent by email to users of the Ovia Fertility, Ovia Pregnancy, and Ovia Parenting mobile apps. Respondents were all 18 years of age or older and living in the United States. The study was granted exemption by our institutional review board. Results Of the 2016 respondents, 39% reported that they were never screened by their healthcare provider for mental health conditions (26% of women with children and 52% of women without children). Among women who reported never being screened by a healthcare provider, 17% reported that they have completed at least one of the screeners (PHQ-9 or Edinburgh Postnatal Depression Scale [EPDS]) in an Ovia mobile app. Of the 2016 respondents, 86% reported being married or in a domestic partnership. Among the single respondents, 32% reported either having children, being pregnant, or currently trying to conceive. More single women who have children, are pregnant, or are actively trying to conceive reported that they would feel most supported by a mobile appl (namely, one of Ovia Health’s three mobile apps) and to seek treatment for mental health concerns compared to married women (19% compared to 14% of married women; P=.03). Additionally, single women who have children, are pregnant, or are actively trying to conceive reported more often than married women that they feel their mental health is best supported by a mobile appl (16% compared to 10% of married women; P=.007). However, both groups of women selected their healthcare provider and their friends/family as the first and second ranking support systems for both seeking mental health treatment and for mental health related support, with the mobile app ranking last. Conclusions Screening for mental health conditions during the reproductive health journey is lacking. Digital solutions that deliver clinically validated screening tools help to screen women who are missed in a clinical setting. Women who report being single throughout parenting, pregnancy, or while trying to conceive find more value in mobile app–provided mental health support compared to married women. These findings highlight two gaps that digital technologies, like Ovia Health, can fill: low mental health screening rates during reproductive years and suboptimal social systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".