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

Identifying subgroups with differential levels of service response to a digital screening and service navigation program for unmet social care needs | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var 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Jalaludin , Kenny Lawson , Siaw-Teng Liaw , Raghu Lingam , Andrew Page , Christa Lam-Cassettari , Katherine Boydell , View ORCID Profile Ping-I Lin , Ilan Katz , Ann Dadich , Shanti Raman , Rebekah Grace , Aunty Kerrie Doyle , Tom McClean , Blaise Di Mento , John Preddy , Susan Woolfenden , View ORCID Profile Valsamma Eapen doi: https://doi.org/10.1101/2025.09.07.25335288 James R John 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia 2 Ingham Institute for Applied Medical Research , Liverpool, NSW, Australia 3 Academic Unit of Infant, Child and Adolescent Psychiatry, South Western Sydney Local Health District , Liverpool, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Teresa Winata 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia 4 National Disability Insurance Scheme Quality and Safeguards Commission , Parramatta, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Si Wang 5 Research and Evaluation Group, The Salvation Army , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melissa Smead 6 Murrumbidgee Local Health District , Wagga Wagga, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Weng Tong Wu 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Weng Tong Wu Jane Kohlhoff 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia 7 Karitane , Carramar, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Virginia Schmied 8 School of Nursing and Midwifery, Western Sydney University , Parramatta, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bin Jalaludin 9 South Western Sydney Local Health District , Liverpool, NSW, Australia 10 School of Population Health, Faculty of Medicine and Health, University of New South Wales , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kenny Lawson 11 School of Medicine, Western Sydney University , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Siaw-Teng Liaw 10 School of Population Health, Faculty of Medicine and Health, University of New South Wales , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Raghu Lingam 12 Population Child Health Research Group, School of Women’s and Children’s Health, Faculty of Medicine, University of New South Wales , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Andrew Page 11 School of Medicine, Western Sydney University , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christa Lam-Cassettari 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia 2 Ingham Institute for Applied Medical Research , Liverpool, NSW, Australia 3 Academic Unit of Infant, Child and Adolescent Psychiatry, South Western Sydney Local Health District , Liverpool, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katherine Boydell 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia 13 Black Dog Institute , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ping-I Lin 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia 14 Department of Psychiatry and Behavioral Neuroscience, School of Medicine, Saint Louis University , Missouri, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ping-I Lin Ilan Katz 15 Social Policy Research Centre, Faculty of Arts, Design, and Architecture, University of New South Wales , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ann Dadich 11 School of Medicine, Western Sydney University , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shanti Raman 9 South Western Sydney Local Health District , Liverpool, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rebekah Grace 16 Transforming early Education and Child Health Research Centre, Western Sydney University , Penrith, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aunty Kerrie Doyle 11 School of Medicine, Western Sydney University , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tom McClean 17 Uniting NSW.ACT , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Blaise Di Mento 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site John Preddy 6 Murrumbidgee Local Health District , Wagga Wagga, NSW, Australia 18 Rural Clinical School, School of Clinical Medicine, University of New South Wales , Wagga Wagga, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Susan Woolfenden 19 Sydney Medical School, Faculty of Medicine and Health, University of Sydney , Sydney, NSW, Australia 20 Sydney Local Health District , Sydney, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Valsamma Eapen 1 School of Clinical Medicine, Faculty of Medicine and Health, Sydney, University of New South Wales , NSW, Australia 2 Ingham Institute for Applied Medical Research , Liverpool, NSW, Australia 3 Academic Unit of Infant, Child and Adolescent Psychiatry, South Western Sydney Local Health District , Liverpool, NSW, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valsamma Eapen For correspondence: v.eapen{at}unsw.edu.au Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Digital tools are increasingly integrated into health systems to identify and support needs, yet their effectiveness remains unclear among priority communities. We hypothesise that responses to the digital intervention might vary based on sociodemographic and psychosocial characteristics. Methods 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 and Murrumbidgee, New South Wales, Australia. Latent class analysis was used to identify subgroups based on clinical and sociodemographic factors. A zero-inflated negative binomial regression was conducted to assess changes in unmet needs, stratified by class and intervention group. Results 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 a 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 a regional/rural site. Class 3 ( n =56) included people of CALD, high SES and education, and good mental health, who were from an 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. Introduction Unmet social care needs, such as financial stress, housing instability, food insecurity, and limited access to social support are critical determinants of health that impact child health and development as well as family wellbeing [ 1 ]. Unmet needs can adversely impact child and family outcomes with consequent poorer health trajectories and long-term intergenerational disadvantage; this highlights the importance of early identification and intervention [ 2 ]. While several services exist to support families with these needs, access and engagement remain inequitable, especially for families facing multiple and intersecting challenges [ 3 ]. Health systems often lack systematic processes to identify and respond to social risks within routine care pathways. Additionally, many families are unaware of the services available to them or struggle to navigate fragmented service systems, compounding their unmet needs [ 4 ]. There is growing interest in the use of digital service navigation tools to help families report needs and receive tailored recommendations or referral support [ 5 , 6 ]. These tools offer the promise of scalable, efficient, and potentially empowering models of care coordination, particularly in resource-constrained environments [ 5 , 6 ]. However, emerging evidence suggests that the success of such tools might vary significantly depending on who uses them and when. Socioeconomic status (SES), cultural and linguistic diversity (CALD), digital literacy, and parental mental health status can all influence whether families engage meaningfully with digital navigation and whether their needs are met [ 7 ]. Watch Me Grow-Electronic (WMG-E) – a digital screening and navigation program, was developed to identify and support child developmental concerns [ 8 ]. The use of this tool was extended to identify and support unmet social care and parental mental health among families with children aged 0-5 years [ 9 ]. This tool has been evaluated, whereby families in the intervention group received tailored digital resources and access to a service navigator providing warm handover to relevant services as per the needs of the families. Families in the control group received care as usual (support from local services/organisations) alongside some general resources without service navigation. While previous evaluations of the WMG-E tool have demonstrated feasibility and acceptability [ 8 , 10 ], less is known about its impact on unmet psychosocial needs and how different subgroups of families respond to the intervention. In particular, it remains unclear whether certain key sociodemographic characteristics (such as child and maternal age, child’s gender, parental education, and cultural background) and clinical profiles (including parental mental health and child developmental vulnerabilities) are associated with unmet need. Moreover, there is a limited understanding of how unmet social needs, such as housing instability, unemployment, financial stress, transportation difficulties, and food insecurity, reflecting the social determinants of health, might interact with these factors to shape differential responses to digital screening and navigation support. To address the knowledge gap, this study aims to identify the distinct subgroups of families and the underlying characteristics that influence their differential responses, if any, to the digital screening and service navigation program. By uncovering the specific contextual, demographic, and psychosocial factors, this study provides insights into the mechanisms that contribute to the effectiveness of the intervention across diverse populations. Findings of this research will also inform the design and implementation of targeted and personalised programs, ensuring they are effective and equitable, especially for families that are underserved or at higher risk of disengagement. Methods Study design and participants This study is a secondary analysis of the WMG-E study, a randomised controlled trial (RCT) to evaluate the effectiveness of a digital screening and service navigation model to identify and address child developmental, parental mental health, and social care needs among two priority population communities: (i) urban and predominantly CALD communities in the South Western Sydney Local Health District (SWSLHD); and (ii) regional/rural communities of the Murrumbidgee Local Health District (MLHD). The authors confirm that all ongoing and related trials for this intervention are registered (ACTRN12621000766819). Participants included parents/carers of a child aged between 6 months and 3 years at the time of enrollment and were recruited via opportunistic contacts (e.g., referrals from child and family health nurses (CFHNs), parenting support programs, and community playgroups, etc.). The recruitment and follow-up period of this study was between 1 August 2021 and 30 June 2023 with the recruitment commencing at the same time across both study sites. Further information on the randomisation and blinding, sample size calculation, and the intervention components are detailed in the published protocol [ 11 ]. Study findings are reported following the Strengthening The Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Measures Latent class indicators Parental psychological distress was measured using the Kessler psychological distress scale (K10) [ 12 ], a standardised questionnaire that assesses psychological distress over the previous 4 weeks. For this study, we used the four categories of the K10 based on the total scores, including likely well (10–19), likely to have a mild mental distress (20–24), likely to have a moderate mental distress (25–29), and likely to have a severe mental distress (30–50). Child developmental vulnerabilities were measured using the Learn the Signs Act Early (LTSAE) [ 13 ], a milestone-checklist used to check children’s social, emotional, language, cognitive, and physical development For this study, we categorised child developmental concerns as no concerns (LTSAE = 0) and one or more concerns (LTSAE ≥1). Sociodemographic variables included child’s age (≤12 months, 13 to 24 months, ≥25 months), child’s gender (male, female), premature birth (no, yes), maternal age (≤30 years, ≥31 years), marital status (married, de facto/single/divorced), parental educational level (high school or below, vocational education training, bachelor’s degree and above), and socioeconomic status (SES) based on the index of relative socio-economic advantage and disadvantage (IRSAD) quintiles (quintile 1 – most disadvantaged to quintile 5 – most advantaged), and current service use (no, yes). Outcome variable Unmet need was assessed using the WE CARE questionnaire [ 14 ], which screens for ten key family psychosocial issues, namely: lack of high school education, unemployment, smoking, drug abuse, alcohol abuse, depression, intimate partner violence, childcare needs, homelessness, and food insecurity. Although unmet needs was a secondary outcome of the RCT, it was used as the outcome variable and to assess associations with latent class membership following the identification of the latent classes for this study. Data analysis The baseline characteristics of the sample were summarised using descriptive statistics. Categorical variables were reported as frequencies and percentages, whereas continuous variables were presented as means and standard deviations. Latent class analysis (LCA) was conducted to identify underlying subgroups of parents based on their sociodemographic and psychosocial characteristics. Models specifying two to five classes were estimated, and model fit was assessed using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), entropy values, and the Lo-Mendell-Rubin adjusted likelihood ratio test (LMR-LRT). The final model was selected based on the optimal balance of fit indices and interpretability. Following class assignment, regression for count/discrete data with clustering within individuals was used to assess changes in social care needs (measured by WE CARE scores) across three time-points (baseline, 6 months, and 12 months). Fixed effects included latent class membership, treatment group (intervention vs control), time, and their interactions. Interaction terms were tested to evaluate differential intervention effects across classes over time by intervention arm. Given that WE CARE scores are discrete count data with greater than expected variability and that approximately 50% of the responses were zero, a zero-inflated negative binomial regression (ZINB) was used to appropriately account for both excess zeros and overdispersion. To account for repeated follow-up observations from the same individuals at different timepoints, clustering was specified at the individual level. All analyses were conducted using R Studio (Version 2023.12.1+402) and Stata v19. Human Ethics and Consent to Participate Declarations The study conforms to the principles outlined in the Declaration of Helsinki. All methods were carried out in accordance with relevant guidelines and regulations of The National Statement on Ethical Conduct in Human Research (2023). The South Western Sydney Local Health District Human Research Ethics Committee approved this study (2020/ETH01418). All participating parents have provided written informed consent prior to participation. Results This study recruited 288 participants with 139 in the control group and 145 in the intervention group (see Table 1 ). View this table: View inline View popup Table 1. Descriptive characteristics of the sample (N=288) Latent Class Analysis Fit statistics for 2-to 5-class models indicated that the 3-class model provided the best balance between fit and parsimony (AIC = 4243.0; BIC = 4436.4; entropy = 0.73), with significant improvement over the 2-class model based on the LMR-LRT ( p < 0.001) (see Table 2 ). The 3-class solution was retained for interpretation. View this table: View inline View popup Table 2. Goodness of fit statistics for 2 to 5-class solutions Class 1 (non-CALD, married, and educated; n =134) primarily included parents older than 30 years of age (61.7%), with high levels of education (72.6% holding postgraduate qualifications), who were predominantly married (80.7% married), middle-class (54.5% in Quintile 3 IRSAD), from the MLHD rural/regional site (89.0%), who were entirely non-CALD population (100.0%), and generally reported good mental health (with 65.7% likely to be well). Class 2 (younger parents, non-CALD, low education and SES with poor mental health; n =94) was comprised of younger parents (70.1% under 30 years of age) with lower education levels (52.3% Year 12 or below), who were predominantly unmarried (82.3% de facto, single, or divorced), almost entirely non-CALD (97.5%), with a lower SES with a notable presence in the most disadvantaged quintiles (27.5% in Quintile 1) from the MLHD rural/regional site (90.0%), and with poor maternal mental health (with 19.6% likely to have severe mental distress). Class 3 (older parents, CALD, high SES, married, highly educated, very good mental health; n =56) included older parents (80.5% over 30 years), who were highly educated (75.9% postgraduate qualifications), with a significant proportion from CALD backgrounds (67.9%), who were married (90.5%), of higher SES (17.0% in Quintiles 4-5) from the SWS urban site (66.0%), with few child developmental concerns (84.9% showing no issues), and with mostly positive mental health (with 74.6% likely to be well; see Table 3 ). View this table: View inline View popup Table 3. Predicted probabilities of class membership for the three-class model LTSAE - Learn the Signs Act Early; CALD – culturally and linguistically diverse; IRSAD - index of relative socio-economic advantage and disadvantage; K10 – Kessler psychological distress scale. Trajectories of social care needs Findings of the mixed-effects models showed that participants in Class 2 had significantly higher social care needs compared to Class 3 (reference group), indicating that this subgroup experienced greater vulnerability. No other significant main effects or interactions were observed between Class memberships and treatment groups (intervention vs. control) over time in (see Fig 1 and Table 4 ). While not statistically significant, there were several notable trends. Compared to Class 3 control participants, participants in the intervention group of class 1 showed a trend towards improvement over time whereas those in Class 2 showed increased needs over time, indicating that the intervention was not effective in this vulnerable group. Download figure Open in new tab Figure 1. Trajectories of Social Care Needs over time by class memberships and group. View this table: View inline View popup Table 4. Trajectories of Social Care Needs by Treatment condition, Time, and Latent Class Membership Interactions Discussion Summary of findings This study identified three distinct latent subgroups of parents based on their sociodemographic and psychosocial characteristics, which shaped their social care needs and responses to the WMG-E tool. Findings showed that Class 2 represented a more vulnerable subgroup, comprised primarily of younger, less-educated, non-CALD parents who were unmarried and predominantly from rural/regional site, with greater socioeconomic disadvantage, including elevated levels of psychological distress, who reported significantly higher unmet needs compared to other classes. Additionally, there were some notable trends in the effectiveness of the intervention that appeared to vary across classes, with vulnerable families in Class 2 facing persistent needs over time. These findings underscore the heterogeneity among family circumstances and emphasise that a one-size-fits-all approach might not effectively address complex, intersecting needs across diverse population groups. This also highlights the critical need for tailored interventions, particularly digital models of care, to better support the challenges and capacities of different family groups. Impact of the intervention by the class membership Analysis of social care needs over time showed that Class 1 participants in the intervention group demonstrated a non-significant but consistent trend of improvement, suggesting greater capacity to engage and act on the digital intervention. Class 3 had low baseline needs and this was maintained over time, consistent with their strong socioeconomic profile, psychosocial supports, and wellbeing. In contrast, Class 2 – representing the most disadvantaged group – reported higher unmet social care needs and a persistent trend of increasing needs post-intervention, indicating that more intensive support might be required beyond the digital intervention alone. While the findings suggest that digital screening and navigation tools, such as WMG-E, can identify and support families with less complex needs, families with multiple, intersecting vulnerabilities might need more intense support, such as face-to-face navigation, co-located services, such as integrated child and family hubs [ 15 , 16 ]. Specifically, digital navigation tools, while scalable and resource-efficient, might not be suitable to address barriers faced by parents with limited digital literacy, social capital, or psychological distress. Hence, it is critical to design and implement interventions within a ‘proportionate universalism framework’ characterised by universal care plus targeted supports commensurate with needs, in a tiered care model with the level and type of support based on the unique child and family-level profiles [ 17 ]. The differential class-specific findings from this study reflect patterns in previous research on digital health tools and service navigation. Research has shown that universal digital interventions often benefit higher SES and digitally literate populations, more than people with complex needs and/or lower digital access [ 18 – 20 ]. The finding that Class 1 participants showed the most consistent but non-significant improvement aligns with the inverse care law [ 21 ], as families with lower needs were more likely to have the resources, time, and confidence to benefit from digital navigation and act on service recommendations. In contrast, the negative trend observed in Class 2 resonates with research that has highlighted the limitations of digital-only solutions in high-need populations. For example, research in primary care and maternal-child health contexts has shown that families facing cumulative stressors, such as young parenthood, financial hardship, and mental health challenges, require intensive, personalised navigation, or case management, rather than self-guided digital tools alone [ 22 – 24 ]. While digital and virtual approaches are attractive from a scalability perspective, they might increase inequitable outcomes if they are implemented as the sole model of care. The increase in reported social care needs among Class 2 participants reflects the concept of ‘diagnostic overload’, where screening identifies issues but fails to provide effective pathways to resolution, posing a risk of increased inequity [ 25 ]. Such outcomes might leave families feeling more overwhelmed or helpless, particularly if they encounter systemic barriers after referral (e.g., long waitlists, language barriers). This interpretation is supported by the qualitative findings from the broader trial, where families reported challenges in following through with digital referrals without hands-on support [ 26 ]. It is also noteworthy that the study was conducted during the COVID-19 pandemic. The associated social restrictions and health service shutdown might have exacerbated challenges in accessing the recommended supports, particularly for families with limited capacity and digital literacy, further preventing the ability to seek and receive support. Finally, this study adds to the emerging literature advocating for stratified or tiered care models, where digital tools are matched with the appropriate level of personal support using hybrid care models based on complexity and risk [ 27 – 29 ]. Tailoring the intensity and modality of interventions to user profiles can enhance both efficiency and equity, particularly in early childhood and family support settings. The findings also raise important considerations around cultural safety and digital inclusion. Class 3 (predominantly CALD families) showed lower social care needs and better mental health profile. While this might be a function of better education and hence better health literacy and capacity to access supports, it remains possible that these families under-reported needs due to cultural stigma, limited trust in health systems, or language barriers. Further, while the WMG-E had language translations available for screening, interpreter availability might enhance accessibility and uptake across culturally diverse populations. Implications for equity and service design The findings of this study suggest that a ‘one-size-fits-all’ intervention might be inappropriate to address social care needs in diverse priority populations. These findings highlight the need for stratified or tiered models of digital care navigation, in which the intensity of support is tailored to participant profiles. For families with fewer barriers to service engagement, digital tools might be sufficient to prompt action and connect them to relevant supports. For families that experience greater disadvantage, however, digital tools must be embedded within more intensive, relationship-based approaches, such as in-person, community health worker programs, case management, and/or co-location of social support services within place-based settings, such as integrated child and family hubs [ 30 ]. Additionally, the results highlight the potential of latent class analysis to inform stratification in digital health intervention models. Designing such models requires explicit attention to the social determinants of health, particularly access and inequity, as well as contextual factors, such as the role of social stigma in some CALD communities, which can shape both engagement with services and outcomes. Integrating brief screening tools at the point of enrolment might allow programs to dynamically allocate families to different levels of navigation support based on the family’s needs, preferences, and capacity to engage with services. Strengths and Limitations A key strength of this study is the use of LCA to uncover heterogeneity in the sample and evaluate differential intervention effects on psychosocial risk factors. Additionally, the use of longitudinal data also allowed for an assessment of change over time, within and between subgroups. Nevertheless, this study has some limitations. First, the class membership was probabilistic and based on observed indicators collected as part of the study, which might not capture all relevant aspects of family complexity or underlying causal relationships. Second, although the sample was drawn from a real-world service setting, the sample size within certain latent classes was relatively small, potentially limiting statistical power for subgroup analyses. Third, social care needs were self-reported and might be subject to response bias or changes in perception over time, particularly after exposure to a needs-assessment tool. Future directions Future studies should explore adaptive intervention designs that modify navigation intensity based on participant characteristics or early engagement patterns. Targeted sampling to include diverse priority population groups is also essential to ensure findings are relevant and equitable. Mixed-methods research – particularly qualitative approaches and arts-based research methods, can illuminate the lived experiences of families in each class, shedding light on barriers to acting on referrals, digital literacy challenges, and the emotional impact of navigating complex systems with minimal support. Moreover, there is a need to pilot-test and evaluate blended models that combine digital tools with human navigation, particularly for populations with higher psychosocial risk. Embedding navigators within trusted community-based or primary care settings and ensuring cultural competence might enhance trust and effectiveness [ 6 ]. Conclusion Digital navigation tools can reach families experiencing service access barriers in resource constrained environments, such as in regional and rural communities. While these tools might be effective for families with less complex needs and good parental capacity, digital service navigation might be insufficient to support families experiencing significant psychosocial adversity. Stratified approaches that leverage a proportionate universalism model (universal plus targeted additional support), matching navigation intensity to participant needs, are essential to promote equity and effectiveness in digital health interventions. Declarations Data Availability The datasets generated and/or analysed during the current study are not publicly available due to ethical and legal reasons, including participant anonymity and privacy, but may be available from the corresponding author on reasonable request. Acknowledgments We would like to express our sincere gratitude to the families who generously shared their time and experiences to participate in this study. Their contributions were invaluable to advancing our understanding of how digital screening and navigation models can better support child development and family wellbeing. We also thank the staff and administrators from South Western Sydney Local Health District (SWSLHD) and Murrumbidgee Local Health District (MLHD) for their support in facilitating recruitment and implementation at each site. In particular, we acknowledge the contributions of the Child and Family Health Services teams, local research governance officers, and service navigator staff who played a vital role in supporting participants throughout the study. Finally, we thank the broader project team and support staff whose efforts in data management, project coordination, and community engagement made this work possible but who are not listed as co-authors. Footnotes Funding: NSW Health COVID-19 Research Grants Round 2. References 1. ↵ Garg A , Marino M , Vikani AR , Solomon BS . 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