Evidence map›Paper›PMID 42879424›Full record

ArticlePLOS digital health2026

The world's oldest living culture leads the way with interpretable generative AI: Aboriginal women shaping the future of perinatal mental health services.

Guanjin Wang, Hachem Bennamoun, Wai Hang Kwok, Rehan Raza, Ferdous Sohel, Bridgette Kelly, Trish Ratajczak, Corinne Reid, Rhonda Marriott, Roz Walker and 2 more

Abstract read
In one paragraph

Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Guanjin WangSchool of Information Technology, Murdoch University, Perth, Western Australia, Australia.ORCID https://orcid.org/0000-0001-7536-5782
Hachem BennamounSchool of Information Technology, Murdoch University, Perth, Western Australia, Australia.
Wai Hang KwokSchool of Nursing and Midwifery, Edith Cowan University, Perth, Western Australia, Australia.
Rehan RazaSchool of Information Technology, Murdoch University, Perth, Western Australia, Australia.
Ferdous SohelSchool of Information Technology, Murdoch University, Perth, Western Australia, Australia.
Bridgette KellyNgangk Yira Institute for Change, Murdoch University, Perth, Western Australia, Australia.
Trish RatajczakNgangk Yira Institute for Change, Murdoch University, Perth, Western Australia, Australia.
Corinne ReidGlobal Health Academy and Academy of Sport, The University of Edinburgh, Edinburgh, United Kingdom.
Rhonda MarriottNgangk Yira Institute for Change, Murdoch University, Perth, Western Australia, Australia.
Roz WalkerNgangk Yira Institute for Change, Murdoch University, Perth, Western Australia, Australia.
Jayne KotzNgangk Yira Institute for Change, Murdoch University, Perth, Western Australia, Australia.
Tracy ReibelNgangk Yira Institute for Change, Murdoch University, Perth, Western Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite their strengths, Aboriginal mothers experience significant health disparities, including perinatal mental health challenges influenced by social, historical, and cultural determinants. Routine risk screening informs follow-up care, yet conventional approaches often fail to address cultural considerations. The Baby Coming You Ready (BCYR) program provides a strengths-based, culturally adapted digital assessment designed to enhance perinatal mental health care. However, some clinicians continue to rely primarily on conventional risk scores, which can neglect broader contextual circumstances influencing Aboriginal mothers, including cultural strengths and protective factors, thereby limiting the program's full impact. This exploratory model-development study examined whether generative AI data augmentation combined with an intrinsically interpretable machine-learning model could support culturally contextualised prediction of perinatal psychological distress among Aboriginal mothers. We analysed deidentified BCYR pilot data from 293 Aboriginal mothers across six Western Australian sites. After excluding five records with incomplete outcome data, the analytic dataset included 288 participants. Models were evaluated using repeated internal train-test splits, with synthetic data used only for training augmentation and performance assessed on held-out real test data. The highest internal performance was observed for the TVAE-augmented Explainable Boosting Machine at an augmentation ratio of 1.0, with an accuracy of 0.867, an F1 score of 0.817, and an AUC of 0.934. Model explanations identified self-blame, loneliness, family pride, day-to-day management, sleep, and related contextual factors as influential contributors to model-estimated risk. These explanations are intended as prompts for culturally safe discussion and clinical reflection. Findings suggest that interpretable generative AI may have potential to support culturally responsive perinatal mental health assessment. However, broader community-governed external validation and prospective clinical workflow evaluation are required before clinical implementation.

Identifiers

PMID42879424
PMCPMC13671517

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.