The last decade has seen a greater transformation in digitalisation, where digital screens have become the most habitual for children, including adults.  These become everyday routines are now interwoven with screens like smartphones, short-video platforms, streaming media, games and online social interaction for children. Here, the key issue is not merely whether children use screens, but how that exposure reshapes attention, language, social learning and “adult-like” awareness in ways that may appear ahead of age. In community settings, caregivers often describe this as a premature or age-inappropriate maturity shift: a perceived change in a child’s understanding, behaviour, or style of interaction that they associate with online content and mobile use.

Current child media guidance increasingly treats screen exposure as a contextual phenomenon. Rather than a single universal limit, the emphasis is placed on what children are watching as well as doing, whether screens displace sleep and offline play, and whether caregivers actively shape the environment through routines, boundaries and co-engagement (American Academy of Paediatrics: 2016). This perspective is especially relevant for Social Work because it locates children’s digital experience inside the family system: supervision capacity, communication patterns, and household constraints determine whether screen use becomes supportive, neutral, or disruptive.

A strong research tradition conceptualises this family role through parental mediation: the strategies caregivers use to regulate and interpret children’s digital/media engagement. Classic typologies distinguish technical/restrictive controls (Rules, filters, and Parental Controls), monitoring/supervision (Checking history, observing use), and active mediation via parent-child discussion about content, risks and values (Livingstone & Helsper: 2008). Earlier evidence in media research also indicates that how parents mediate can matter: restrictive and active forms of mediation show different relationships with children’s outcomes and interpretations of media exposure (Nathanson: 1999). In practical aspect, controls and monitoring may reduce certain exposures, while open discussion may strengthen children’s critical thinking and self-regulation, making it a plausible protective mechanism against undesirable developmental shifts.

The Socio-economic conditions provide the wider structure within which mediation occurs. Income and occupational context can shape access to devices, the time available for supervision, digital literacy, and the feasibility of consistent monitoring and guidance. At the population level, major child-focused reports argue that digital environments can amplify inequalities—both through unequal access and unequal capacity to manage risks and opportunities (UNICEF: 2017; UNICEF Innocenti: 2025). Against this background, examining income-group differences in parental controls, monitoring and parent–child dialogue and how these relate to caregivers’ reports of premature maturity shifts, offers an applied, policy-relevant evidence base for preventive family interventions and digital-parenting support in Social Work practice.

2. 2. Object of the Research Study:

• To assess the prevalence and pattern of screen exposure–related perceived age-inappropriate (premature) maturity shifts in children, and to test whether these reported shifts are associated with parental mediation practices (Like: controls/filtering, monitoring history, and open parent–child discussion).

• To examine whether annual family income groups differ in

(a) Parental mediation practices (Like: controls/filtering, monitoring, open discussion)

(b) The reporting of screen exposure–related perceived age-inappropriate (premature)       maturity shifts in children.

3. Hypothesis:

H1: Parents’ reporting of screen exposure–related perceived age-inappropriate (premature) maturity shifts in children is significantly associated with parental mediation practices,

H2: Annual family income groups differ significantly in:

(a)       Parental mediation practices (controls/filtering, monitoring, open discussion), and

(b)       Parents’ reporting of screen exposure–related perceived age-inappropriate    (premature)          maturity shifts in children.

3. Literature Reviews:

(Livingstone & Helsper: 2008), mostly focused on parental control through mediation in children's internet use. The structured interview was undertaken through a national survey in a domestic home interview. Mediation dimension & online experience as well as the risk involved in the study. The children between 9 and 19 years old in the national sample are selected at random across the nation. The major findings indicated that different mediation forms do not automatically eliminate risk, and it also depends on the pattern of mediation & operation in daily life.

(Nielsen: 2019), study key focused on linking parental mediation practices to adolescents’ problematic screen use. The study included the quantitative analysis, which links mediation styles to problematic internet use & problematic online gaming. The comparing analysis of patterns observed in the study rather than assuming the best method for the parental mediation practice. The adolescent population and sample used for the study were mediation categories & outcomes of adolescence covered. The conclusion of the study measures that no single mediation style consistently fixes problematic use, where the effect varies by behaviour types and context.

(Smahel: 2020), studied based on the child safety research, which covered the measures of family factors and internet access, practice, risk, as well as opportunities. The areas of research covered the 19 European countries under 9 to 16-year-old children during 201- to 2019. The finding showed the digital experience of children according to age & context, where household context & socio-economic position should be treated as structural conditions shaping exposure and supervision.

(Hernandez: 2023), studied parental monitoring of early adolescent social technology use, where an explanatory mixed method was used during the period. here quantitative modelling and qualitative tools are used. The mixed method design explicitly connects monitoring style to broader family functioning. The output of the study indicated that monitoring is not only one thing. while restrictive monitoring correlated with the problems in some of the context during the period. Further supportive and active approaches related to different.

(Vossen: 2024),   Studies focused on the parenting & adolescents’ problematic social media use under the systematic review. The study highlights the parenting factors like monitoring, rules and broader parenting dimensions with the problematic social media use by adolescents. This multitude considered the mixed methods where parenting included various inputs, not limited to single variables. The conclusion of the study indicates that different form of mediation acts differently rather than in a single direction.                                                                                                                                                        

(Tan: 2025), considered 88 research studies included under the digital parenting & children’s digital wellbeing with meta-analysis. The samples consist of mixed with child & adolescents. Here, digital parenting is generally linked to lower negative digital wellbeing and positive mediation which tends to positive outcomes.

(Giovanelli .: 2025), the systematic review based research focused on the screen use by children and adolescents and their parental mediation. This study represented multi study review under considered child-adolescent samples during period.

4. Research Methods & Tools:

The present research study uses a cross-sectional, descriptive–analytical design to address the two locked objectives and hypotheses. Screen exposure is treated as the everyday context of children’s screen/mobile use. The key outcome is defined as a parent-reported, screen exposure–related perceived age-inappropriate (premature) maturity shift in the child.

The data analysis is based on a completed survey dataset of 450 parent/guardian responses from Vadodara district in Gujarat. Each record represents one household/child entry as captured by the questionnaire. Eligible cases include parents/guardians who provided a valid response on the maturity-shift outcome. Records with missing outcome values are excluded from outcome-based tests. Missing values in predictors are reported and handled transparently, with list wise exclusion used for specific tests unless otherwise stated.

Data were collected through a structured close-ended questionnaire. The dependent variable is the perceived age-inappropriate (premature) maturity shift attributed to screen exposure. If the item is coded as Yes/No, it is analysed as a binary outcome. If the item is recorded in ordered categories, it is described using the original categories, and dichotomised only when required for regression, with the cut-off clearly stated.

Parental mediation is measured using three separate indicators. These include parental controls or filtering of the child’s mobile activities, monitoring or checking the child’s mobile history/activities, and open parent–child discussion about mobile use and online activities. Each is analysed as a Yes/No variable. The indicators are kept separate because they represent different mechanisms: technical restriction, supervision, and active communication.

Socio-economic context is represented through annual family income groups and father’s occupation categories. Father’s occupation is used mainly for descriptive profiling and as a structural covariate in adjusted analyses. A mediation index can also be created to summarise cumulative mediation. This index ranges from 0 to 3 by summing controls, monitoring, and discussion after coding each as 0/1. The index is optional and used only to support interpretation; the three mediation components remain central.

Descriptive analysis reports frequencies and percentages for all study variables. The prevalence of perceived maturity shift is presented overall and across income groups. Hypothesis testing begins with cross-tabulations. For H1, associations between the maturity-shift outcome and each mediation practice are tested using chi-square tests. Effect sizes are reported using Cramér’s V. For H2, income group differences are tested using chi-square analyses for income versus each mediation practice, and income versus maturity shift, again reporting Cramér’s V. Adjusted analysis is conducted using binary logistic regression when the outcome is binary. The maturity-shift outcome is entered as the dependent variable. Controls/filtering, monitoring, and open discussion are entered as predictors, with income group and father’s occupation included as covariates. Results are reported as adjusted odds ratios with 95% confidence intervals and p-values.

Reliability statistics such as Cronbach’s alpha are not essential when mediation indicators are single Yes/No items. Reliability assessment becomes relevant only if a larger multi-item scale is constructed.      

The Interpretation emphasises direction and magnitude, not only statistical significance. The significance threshold is set at α = 0.05 (two-tailed). Because multiple chi-square tests are used, findings are interpreted alongside effect sizes rather than p-values alone. Data Analyses carried out by Python:3.2. The minimum reporting set includes frequency tables, chi-square crosstabs with effect sizes, and logistic regression outputs where applicable.

5. Results & Discussion:
6. Descriptive Analysis:

The comparing control-filtering parents with maturity shift in child represents the relations as well as placing of control in digital screen tends to lower the maturity shift which is clearly visible in Table:1.

Table:1 Comparing Maturity Shift in Child with Control-Filtering by Parents

Source: Primary Descriptive Analysis by Author

Here, an observed 26.8 per cent maturity shift among families, against 73.2 per cent, has been no maturity shift among children during the period. Further, 29.4 percent families has maturity shift but not used any controls or filtering by parents. While in the state, out of 450 samples, 164 families used controls or filtering for the digital screen, while 286 families did not use any controls.

Figure:1 Maturity Shift Rate by Parental Control/Filtering & Monitoring-History Checking

Table: 2 showing the maturity shift with monitoring history of child, where monitoring history. Here, comparison between parents who monitor the child’s device history/usage and the reporting of screen exposure–related perceived premature maturity shift indicates a clearer separation than what is typically seen with technical controls alone. Among families who reported monitoring history, 23.0% of children were reported to show a maturity shift, while 77.0% were reported to show no maturity shift. In contrast, among families who did not monitor the child’s device history, the maturity shift rate rises to 34.8%, with 65.2% reporting no maturity shift.

Table:2 Comparing Maturity Shift in Child with Monitoring History

Source: Primary Descriptive Analysis by Author

This pattern suggests that monitoring is associated with a lower prevalence of perceived premature maturity shifts, implying that routine supervision may limit children’s exposure to age-inappropriate content or reduce unsupervised digital behaviour that parents later interpret as “maturity before age.” In the aggragate sample (n = 450), 243 families noted monitoring practices, while 207 families reported no monitoring, indicating that monitoring is relatively common and empirically meaningful as a differentiating family practice in this observation.

The Table:3 examines whether open parent–child discussion about mobile use and online activities is linked with the reporting of screen exposure–related perceived age-inappropriate (premature) maturity shift. The pattern here is distinctly different from the monitoring table. Among families who reported open discussion, 42.0% of children were reported to show a maturity shift, while 58.0% were reported to show no maturity shift.

Table 3. Comparing Maturity Shift in Child with Open Parent–Child Discussion

Source: Primary Descriptive Analysis by Author

In contrast, among families who reported no open discussion, the maturity shift rate is markedly lower at 22.9%, with 77.1% reporting no maturity shift. In other words, maturity shift reporting is higher in households where open discussion is present, not lower.

Here, Table 4 shows how the reporting of screen exposure–related perceived age-inappropriate (premature) maturity shift varies across annual family income groups. A clear income gradient is visible in the descriptive pattern.

Table: 4: Comparing Maturity Shift in Child with Annual Income

Source: Primary Descriptive Analysis by Author

The income-wise distribution shows a clear gradient in the reporting of screen exposure–related perceived age-inappropriate (premature) maturity shift. The 0–2 lakh income group reports the highest maturity shift (40.0%), followed by the 2–5 lakh group (32.5%). Reporting declines substantially in the 5–10 lakh group (22.0%) and remains at a comparable level in the 10 lakh+ group (23.0%). Overall, the pattern indicates that maturity-shift reporting is more common in lower-income households and least common in the mid-to-higher income band (5–10 lakh). This gradient is plausibly shaped by structural conditions rather than any simple parenting deficit. Lower-income families may face tighter time budgets, fewer supervised leisure alternatives, and reduced capacity for continuous oversight, which can increase unsupervised or unstructured screen exposure. At the same time, differences in parental awareness and thresholds for labelling behaviour as “premature maturity” may also contribute to income-linked variation in reporting.

Table: 5: Comparing Parents Discussion with Children & Annual Income

Source: Primary Descriptive Analysis by Author

The Table:5 indicates that open parent–child discussion about mobile use is present in 131 of 450 households (29.1%), suggesting that roughly three in ten families report active dialogue as a mediation strategy, while seven in ten (70.9%) do not report such discussion. Across income categories, the prevalence of discussion varies modestly. The 0–2 lakh group shows the highest reported discussion (32.85%), followed closely by the 5–10 lakh group (31.0%). The 10 lakh+ group reports discussion in 27.9% of households, while the 2–5 lakh group shows the lowest level (25.83%).

Table: 6: Comparing Parents Monitoring with Children & Annual Income

Source: Primary Descriptive Analysis by Author

Here, Table 6 describes how parents’ monitoring of a child’s device usage/history varies across annual family income groups. The overall level is 243 of 450 households report monitoring, while 207 report no monitoring. This gives an aggregate monitoring prevalence of 54.0%, meaning a little more than half of families use monitoring as a parental mediation practice. Across income categories, the pattern is uneven and shows a clear contrast between the lowest-income group and the rest. Monitoring is lowest in the 0–2 lakh group (31.4%). It increases sharply in the 2–5 lakh group (53.0%) and reaches the highest level in the 5–10 lakh group (62.5%). The 10 lakh+ group (59.0%) remains high and close to the 5–10 lakh group. Overall, monitoring appears more common in middle and higher-income households than in the lowest-income households.

Table: 7: Comparing Parents Control & Annual Income

Source: Primary Descriptive Analysis by Author

Here Table: 7 represents the distribution of parental controls/filtering (technical restrictions on the child’s mobile activities) across annual family income groups. At the aggregate level, 164 out of 450 households report using controls, while 286 households report no controls. This corresponds to an overall prevalence of 36.4% (≈ 36.44% in your table). In simple terms, about one-third of families use technical controls, while two-thirds do not. Across income groups, the pattern is mixed rather than linear.  The highest use appears in the lowest-income group, but the remaining three groups cluster within a relatively narrow band in the low-to-mid 30% range.

1. Hypothesis Results:

Table: 8

Chi-square Test: Association Premature Maturity Shift with Parental Mediation Practices

Source: Data Analysis by Author

H1: Parents’ reporting of screen exposure–related perceived age-inappropriate (premature) maturity shifts in children is significantly associated with parental mediation practices,

Table 8 showing results of Chi-square Test where a differentiated mediation pattern described here rather than a uniform effect across all practices. Controls/filtering does not demonstrate a statistically meaningful association with the maturity-shift outcome (χ²(1)=0.218, p=0.641; Cramér’s V=0.022), indicating that technical restriction alone is not a discriminating factor for maturity-shift reporting in this sample.

In contrast, monitoring history/usage shows a statistically significant association with maturity-shift reporting (χ²(1)=7.000, p=0.008; Cramér’s V=0.125), and the effect size falls in the small range. Open parent–child discussion displays the strongest association (χ²(1)=15.721, p<0.001; Cramér’s V=0.187), representing a small-to-moderate relationship by common interpretive conventions.

Decision on H1: The hypothesis is supported in principle, but partially, because significant associations are observed for monitoring and open discussion, while controls/filtering is not significant. Final conclusion that parental mediation is associated with perceived maturity shift, but the association is driven mainly by monitoring and parent–child dialogue rather than technical controls.

H2: Annual family income groups differ significantly in:

(a)          Parental mediation practices (controls/filtering, monitoring, open discussion), and

(b)          Parents’ reporting of screen exposure–related perceived age-inappropriate (premature)          maturity shifts in children.

Table: 9

Chi-square Test: Difference in Control/Filtering, Monitoring-History, Open Discussion, Maturity                           Shift with Income Groups

Source: Data Analysis by Author

The Table:9 Representing the findings indicate that income stratification is selective, not universal across all mediation domains. Income groups show a clear and statistically significant difference in monitoring practices (χ²(3)=19.925, p<0.001; Cramér’s V=0.210), reflecting a small-to-moderate association. In practical terms, monitoring is more prevalent in middle-to-higher income bands and lowest in the lowest-income band, suggesting that routine supervision may be influenced by structural resources such as time availability, device literacy, and access to monitoring features.

In contrast, income-group differences are not statistically supported for controls/filtering (χ²(3)=3.362, p=0.339; Cramér’s V=0.086) or for open parent–child discussion (χ²(3)=4.704, p=0.195; Cramér’s V=0.102). These small effect sizes imply that technical restriction and conversational mediation are relatively less income-stratified in this dataset than monitoring behaviour.

For the outcome, income groups differ significantly in the reporting of perceived premature maturity shift (χ²(3)=10.090, p=0.018; Cramér’s V=0.150), indicating a small but meaningful association. Descriptively, the lowest-income group reports the highest maturity-shift prevalence, while the mid-to-higher group (5–10 lakh) shows the lowest. This pattern is consistent with a structural interpretation: differences in supervision capacity, routine stability, alternative recreational options, and exposure contexts may shape both children’s digital environments and parents’ interpretation of “maturity before age.” However, the effect size indicates that income is one contributor among several; it does not function as a sole determinant.

Decision on H2: The hypothesis is supported overall, but partially. Income groups differ significantly in monitoring practices and in maturity-shift reporting, while differences in controls/filtering and open discussion are not statistically supported.  Major conclusion that income stratifies some forms of parental mediation (especially monitoring) and is associated with differences in maturity-shift reporting, but income does not uniformly differentiate all mediation practices in this sample.

C Binary logistic regression:

*Model Aim: To estimate the association between mediation practices and maturity              shift after adjusting for income group

Outcome(Y)= Maturity Shift

Predictors: Controls, Monitoring, Discussion

Covariates: Annual Family Income Groups, Father Occupation

Total Sampling: 450 Households

Table: 10 Binary Logistic Model:

Source: Data Analysis by Author

Likelihood-ratio χ² (df=10) = 43.871, p = 0.00000347

Pseudo R² = 0.0816

The binary logistic regression model estimates how each parental mediation practice is associated with the odds of reporting a screen exposure–related perceived premature maturity shift, while holding other factors constant. After adjustment for socio-economic context (income group) and father’s occupation, open parent–child discussion is the only mediation factor that remains a statistically reliable predictor. The AOR for open discussion is 2.238 (95% CI: 1.364–3.672; p = 0.001). This means that households reporting open discussion have about 2.2 times higher odds of reporting premature maturity shift compared to households without open discussion, net of the other predictors in the model.

In contrast, controls/filtering does not show an independent association with the outcome once other variables are controlled. Its AOR is 0.915 (95% CI: 0.533–1.569; p = 0.747). This estimate is close to 1.0 and the confidence interval is wide and crosses 1.0. Statistically and practically, this indicates no clear evidence that technical controls alone reduce or increase the odds of maturity-shift reporting.

Similarly, monitoring/checking history does not remain significant in the adjusted model. Its AOR is 0.670 (95% CI: 0.401–1.119; p = 0.126). The direction suggests lower odds in monitoring households, but the uncertainty is large and the CI crosses 1.0. Therefore, the monitoring does not show a stable independent effect after adjustment, even though it appeared significant in bivariate analysis.

The model supports a specific evidence-based conclusion: the strongest independent marker linked to maturity-shift reporting is open parent–child discussion, not technical controls or monitoring. This does not mean discussion causes premature maturity. A more defensible interpretation is that discussion often reflects problem recognition and response. Parents may initiate or intensify discussion after observing changes they interpret as premature maturity. This creates a strong association in cross-sectional data.

Overall, the regression indicates that, after adjusting for socio-economic factors, open parent–child dialogue remains significantly associated with the reporting of screen-related premature maturity shifts, whereas controls/filtering and monitoring do not show independent associations.

6. Conclusion:

Under the present research study, the cross-sectional sample of 450 households, 28.4% of parents reported a screen exposure–related age-inappropriate (premature) maturity shift in their child. The study’s evidence shows that this outcome is not strongly separated by technical controls/filtering, but it is linked to monitoring and especially to open parent–child discussion. Controls/filtering had no meaningful association with maturity shift (χ² p=0.641), whereas monitoring (χ² p=0.008) and open discussion (χ² p<0.001) showed significant relationships. In the adjusted logistic model, open discussion remained the only independent predictor (AOR=2.238; 95% CI: 1.364–3.672; p=0.001), while controls and monitoring did not retain independent effects after adjustment. This pattern suggests that family communication often reflects problem recognition and response, not necessarily prevention. Further, Income differences were selective. Income groups differed significantly in monitoring practices (χ² p<0.001) and in maturity-shift reporting (χ² p=0.018), with the highest maturity-shift prevalence in the 0–2 lakh group and the lowest in the 5–10 lakh group. Income did not show strong differences for controls/filtering or open discussion. Overall, the study concludes that screen-related premature maturity concerns are associated more with supervision and parent–child interaction than with technical restriction alone, and that structural household context shapes the distribution of supervision practices and the reporting of maturity-related changes.

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