mHealth and Perinatal Depression in Low-and Middle-Income Countries: A Scoping Review of the Literature
Bibliographic record
Abstract
Women in low- and middle-income countries have high rates of perinatal depression. As smartphones become increasingly accessible around the world, there is an opportunity to explore innovative mHealth tools for the prevention, screening, and management of perinatal depression. We completed a scoping review of the literature pertaining to the use of mobile phone technologies for perinatal depression in low-and middle-income countries. PubMed CINHAL, and Google Scholar databases were searched, generating 423 results. 12 articles met our inclusion criteria. Two of the 12 articles reviewed mobile phone applications. The remaining 9 articles were study protocols or descriptive/intervention studies. Our results reveal that minimal literature is currently available on the use of mobile health for perinatal depression in low- and middle-income countries. We found four articles that present the results of an intervention that were delivered through mobile phones for the treatment of perinatal depressive symptoms and an additional qualitative study describing the perceptions of mothers receiving cognitive behavioral therapy via telephones. These studies demonstrated that depressive symptoms improved after the interventions. There is potential to improve the quality of mHealth interventions, specifically mobile phone applications for perinatal depressive symptoms and depression, through meaningful collaborative work between healthcare professionals and application developers.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".