A Social Media Group Cognitive Behavioral Therapy Intervention to Prevent Depression in Perinatal Youth: Stakeholder Interviews and Intervention Design
Bibliographic record
Abstract
BACKGROUND: Adolescents and young adults aged <25 years (youth) are at a higher risk of perinatal depression than older adults, and they experience elevated barriers to in-person care. Digital platforms such as social media offer an accessible avenue to deliver group cognitive behavioral therapy (CBT) to perinatal youth. OBJECTIVE: We aim to develop the Interactive Maternal Group for Information and Emotional Support (IMAGINE) intervention, a facilitated social media group CBT intervention to prevent perinatal depression in youth in the United States, by adapting the Mothers and Babies (MB) course, an evidence-based in-person group CBT intervention. In this study, we report perspectives of youth and health care providers on perinatal youths' mental health needs and document how they informed IMAGINE design. METHODS: We conducted 21 semistructured in-depth individual interviews with 10 pregnant or postpartum youths aged 14-24 years and 6 health care workers. All interviews were recorded, transcribed, and analyzed using deductive and inductive approaches to characterize perceptions of challenges and facilitators of youth perinatal mental health. Using a human-centered design approach, stakeholder perspectives were incorporated into the IMAGINE design. We classified MB adaptations to develop IMAGINE according to the Framework for Modification and Adaptation, reporting the nature, timing, reason, and goal of the adaptations. RESULTS: Youth and health care workers described stigma associated with young pregnancy and parenting, social isolation, and lack of material resources as significant challenges to youth mental wellness. They identified nonjudgmental support, peer companionship, and access to step-by-step guidance as facilitators of youth mental wellness. They endorsed the use of a social media group to prevent perinatal depression and recommended that IMAGINE facilitate peer support, deliver content asynchronously to accommodate varied schedules, use a confidential platform, and facilitate the discussion of topics beyond the MB curriculum, such as navigating support resources or asking medical questions. IMAGINE was adapted from MB to accommodate stakeholder recommendations and facilitate the transition to web-based delivery. Content was tailored to be multimodal (text, images, and video), and the language was shortened and simplified. All content was designed for asynchronous engagement, and redundancy was added to accommodate intermittent access. The structure was loosened to allow the intervention facilitator to respond in real time to topics of interest for youth. A social media platform was selected that allows multiple conversation channels and conceals group member identity. All adaptations sought to preserve the fidelity of the MB core components. CONCLUSIONS: Our findings highlight the effect of stigmatization of young pregnancy and social determinants of health on youth perinatal mental health. Stakeholders supported the use of a social media group to create a supportive community and improve access to evidence-based depression prevention. This study demonstrates how a validated intervention can be tailored to this unique group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".