Psychological interventions for maternal depression among women of African and Caribbean origin: a systematic review
Bibliographic record
Abstract
BACKGROUND: Maternal depression is a leading cause of disease burden for women worldwide; however, there are ethnic inequalities in access to psychological interventions in high-income countries (HICs). Culturally appropriate interventions might prove beneficial for African and Caribbean women living in HICs as ethnic minorities. METHODS: The review strategy was formulated using the PICo (Population, phenomenon of Interest, and Context) framework with Boolean operators (AND/OR/NOT) to ensure rigour in the use of search terms ("postpartum depression", "maternal depression", "postnatal depression", "perinatal depression" "mental health", "psychotherapy" "intervention", "treatment", "black Caribbean", "black African", "mothers" and "women"). Five databases, including Scopus, PsycINFO, Applied Social Science Index and Abstracts (ASSIA), ProQuest Central and Web of Science, were searched for published articles between 2000 and July 2020. 13 studies met the inclusion criteria, and the relevant data extracted were synthesised and thematically analysed. RESULTS: Data syntheses and analyses of included studies produced four themes, including (1) enhance parenting confidence and self-care; (2) effective mother-child interpersonal relationship; (3) culturally appropriate maternal care; and (4) internet-mediated care for maternal depression. CONCLUSION: In the quest to address maternal mental health disparities among mothers of African and Caribbean origin in HICs, the authors recommend culturally adapted psychological interventions to be tested in randomised control trials.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".