# Identifying subgroups with differential levels of service response to a digital screening and service navigation program for unmet social care needs

**Authors:** James R. John, Teresa Winata, Si Wang, Melissa Smead, Weng Tong Wu, Jane Kohlhoff, Virginia Schmied, Bin Jalaludin, Kenny Lawson, Siaw-Teng Liaw, Raghu Lingam, Andrew Page, Christa Lam-Cassettari, Katherine Boydell, Ping-I. Lin, Ilan Katz, Ann Dadich, Shanti Raman, Rebekah Grace, Aunty Kerrie Doyle, Tom McClean, Blaise Di Mento, John Preddy, Susan Woolfenden, Valsamma Eapen, Onaedo Ilozumba, Onaedo Ilozumba, Onaedo Ilozumba, Onaedo Ilozumba

PMC · DOI: 10.1371/journal.pone.0332790 · 2026-01-07

## TL;DR

This study finds that a digital health intervention for unmet social care needs works better for some families than others, depending on their background and mental health.

## Contribution

The study identifies subgroups of families with differing responses to digital interventions based on sociodemographic and psychosocial factors.

## Key findings

- Three distinct family subgroups were identified based on mental health, education, and location.
- Class 2 participants had significantly higher unmet needs compared to Class 3, showing the intervention was less effective for vulnerable groups.
- Digital tools may not be sufficient for families with higher psychosocial adversity, suggesting a need for tiered support systems.

## Abstract

Digital screening and navigation interventions are increasingly integrated into health systems to identify and support families’ unmet social care needs, yet their effectiveness in improving outcomes remains unclear among priority population communities. We hypothesise that responses to such digital interventions might vary based on sociodemographic and psychosocial characteristics.

Data were analysed from 288 participants in a randomised controlled trial evaluating Watch Me Grow-Electronic – a digital screening and service navigation model to identify psychosocial needs, parental wellbeing, and child developmental needs in South Western Sydney (urban site) and Murrumbidgee (regional/rural site), New South Wales, Australia. Latent class analysis was used to identify subgroups of families based on parental and child clinical and sociodemographic factors. A zero-inflated negative binomial regression was conducted to assess changes in unmet needs, stratified by class and intervention group.

Three distinct classes were identified. Class 1 (n = 134) included people who were entirely non-culturally and linguistically diverse (CALD) background, in good mental health, with higher education and socioeconomic status (SES), and from the regional/rural site. Class 2 (n = 94) included people who were predominantly non-CALD, of low education and SES, had poor mental health, and from the regional/rural site. Class 3 (n = 56) included people of CALD, high SES and education, and good mental health, who were from the urban site. Compared to the Class 3, participants in Class 2 showed significantly higher needs, indicating that the intervention was not effective in this vulnerable group.

Digital navigation tools might support families that experience lower psychosocial adversity but are insufficient for families that experience higher levels of adversity, highlighting the need for tiered approaches to ensure equity.

## Full-text entities

- **Diseases:** mental (MESH:D008607), COVID-19 (MESH:D000086382), ASSOCIATED WITH (MESH:D018886), depression (MESH:D003866), alcohol abuse (MESH:D000437), drug abuse (MESH:D019966), mentally ill (MESH:D001523), CALD (MESH:C580335), mental distress (MESH:D012128), food (MESH:D005517)
- **Chemicals:** PONE-D-25-48221R2 (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Figures

5 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12779123/full.md

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Source: https://tomesphere.com/paper/PMC12779123