Perinatal depression and anxiety in Ghana: a qualitative study of women's perspectives on AI-driven interventions
Bibliographic record
Abstract
BACKGROUND: The perinatal period is critical for maternal and child health, yet many women experience perinatal mental illnesses, particularly perinatal depression and anxiety. In Ghana, the burden of perinatal depression and anxiety is exacerbated by socioeconomic challenges and limited access to mental health services. This study explores the perceptions and experiences of pregnant women and new mothers regarding the role of artificial intelligence in addressing perinatal depression and anxiety. METHODS: A qualitative approach utilizing focus group discussions was employed to gather insights from 15 participants, including 8 pregnant women and 7 new mothers, in Accra, Ghana. Thematic analysis was used to identify key themes related to experiences with perinatal depression and anxiety and attitudes toward artificial intelligence technologies. RESULTS: Four main themes emerged: awareness of perinatal depression and anxiety and its emotional impact, scepticism and fear regarding artificial intelligence's ability to provide emotional support, perceived benefits and significant barriers associated with artificial intelligence tools. The participants acknowledged the potential for artificial intelligence to aid in self-monitoring and education but expressed concerns about privacy, trust, and the fear of losing human interaction in care. CONCLUSION: This study highlights the complex interplay of awareness, emotional experiences, and attitudes toward artificial intelligence among perinatal women in Ghana. This highlights the need for culturally sensitive educational initiatives and ethical guidelines for artificial intelligence integration in maternal health, aiming to enhance mental health outcomes for women and their families in low- and middle-income contexts.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".