Evidence map›Paper›PMID 41499590›Full record

Trial reportPloS one2026

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

James R John, Teresa Winata, Si Wang, Melissa Smead, Weng Tong Wu, Jane Kohlhoff, Virginia Schmied, Bin Jalaludin, Kenny Lawson, Siaw-Teng Liaw and 15 more

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

25 authors.

James R JohnSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Teresa WinataSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Si WangResearch and Evaluation Group, The Salvation Army, Sydney, New South Wales, Australia.
Melissa SmeadMurrumbidgee Local Health District, Wagga Wagga, New South Wales, Australia.
Weng Tong WuSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0009-0004-8483-739X
Jane KohlhoffSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Virginia SchmiedSchool of Nursing and Midwifery, Western Sydney University, Parramatta, New South Wales, Australia.
Bin JalaludinSouth Western Sydney Local Health District, Liverpool, New South Wales, Australia.
Kenny LawsonSchool of Medicine, Western Sydney University, Sydney, New South Wales, Australia.
Siaw-Teng LiawSchool of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Raghu LingamPopulation Child Health Research Group, School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.
Andrew PageSchool of Medicine, Western Sydney University, Sydney, New South Wales, Australia.
Christa Lam-CassettariSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Katherine BoydellSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Ping-I LinSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-9739-7184
Ilan KatzSocial Policy Research Centre, Faculty of Arts, Design, and Architecture, University of New South Wales, Sydney, New South Wales, Australia.
Ann DadichSchool of Medicine, Western Sydney University, Sydney, New South Wales, Australia.
Shanti RamanSouth Western Sydney Local Health District, Liverpool, New South Wales, Australia.
Rebekah GraceTransforming early Education and Child Health Research Centre, Western Sydney University, Penrith, New South Wales, Australia.
Aunty Kerrie DoyleSchool of Medicine, Western Sydney University, Sydney, New South Wales, Australia.
Tom McCleanUniting NSW.ACT, Sydney, New South Wales, Australia.
Blaise Di MentoSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
John PreddyMurrumbidgee Local Health District, Wagga Wagga, New South Wales, Australia.
Susan WoolfendenSydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Valsamma EapenSchool of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-6296-8306

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDigital 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.

methodsData 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.

resultsThree 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. DISCUSSION: 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.

Indexed as

Health Services Needs and DemandAdultChildFemaleHumansMaleMental HealthMiddle AgedNew South Wales

Identifiers

PMID41499590
PMCPMC12779123

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.