Pornography Use, Motivational Patterns and Suicidal Ideation Among Young Adults: A Cross-sectional Behavioural Analysis

Abstract

Background

Pornography engagement is frequently examined through exposure-based frameworks that assume higher levels of consumption are associated with adverse psychological outcomes. Emerging evidence suggests that such engagement reflects underlying reward-processing and affect-regulation mechanisms. However, prior findings regarding its association with suicidal ideation remain inconsistent.

Purpose

The present study aimed to examine the association between pornography exposure, consumption motives, depressive symptoms and suicidal ideation among young adults.

Methods

A cross-sectional sample of 541 university students completed measures of pornography exposure intensity, pornography consumption motives, depressive symptoms and suicidal ideation. Bayesian regression analyses were conducted to examine associations between pornography use, motives, depressive symptoms and suicidal ideation.

Results

Depressive symptoms emerged as the most robust predictor of suicidal ideation across all models. Pornography exposure intensity demonstrated a small negative association with suicidal ideation (β = −0.09, 94% highest density interval (HDI) [−0.15, −0.03]) after accounting for depressive vulnerability. Among motivational factors, emotional avoidance was positively associated with suicidal ideation (β = 0.15, 94% HDI [0.06, 0.23]), whereas sexual pleasure motives were negatively associated (β = −0.18, 94% HDI [−0.29, −0.08]) with suicidal ideation. A significant interaction was observed between depressive symptoms and sexual curiosity.

Conclusion

The findings suggest that pornography use is not uniformly associated with suicidal ideation. Instead, associations vary depending on various psychological factors and motives for use. As the study is cross-sectional, the findings should be interpreted as associational rather than causal. These results highlight the importance of considering emotional regulation and individual vulnerability when examining the mental health implications of pornography use.

Introduction

Emerging adulthood represents a developmental stage characterised by identity exploration, increased autonomy and heightened engagement with digital media environments. ¹ This developmental context is particularly relevant to understand how young adults engage in identity formation related to sexual identities, relational expectations and developing strategies for managing emotions within media ecosystems that enable constant, individualised and private content access. Unlike earlier eras, in which sexually explicit material required deliberate effort to obtain, online pornography is now ubiquitously accessible through mobile devices, streaming platforms and participatory digital environments. Among the various forms of digital media consumption prevalent during this stage, online pornography has become one of the most widely accessed yet socially contested forms of media engagement. Given this increased accessibility, understanding how pornography use relates to psychological well-being has become an important area of research.

Prior research on mental health focused on problematic pornography use, conceptualised in an addiction framework, and its relation to psychological disorders such as loneliness, depression and suicidality.^{2, 3} Other studies reported weak or inconsistent associations between pornography exposure frequency and negative mental health outcomes, particularly when controlling for religiosity, moral incongruence or underlying psychological distress.^4–6 However, the findings are inconsistent and polarised, which signals deeper conceptual issues arising from different theoretical frameworks and operationalisation of the construct itself. This divergence suggests the need for a more nuanced understanding of pornography use.

A key limitation in prior research lies in the tendency to treat pornography use as a monolithic construct. Exposure intensity (e.g., frequency or duration) is often analysed without differentiating the motivational processes underlying engagement. In contrast to addiction-oriented models, Uses and Gratifications Theory suggests that individuals choose media to fulfil psychological needs such as emotional regulation, hedonistic gratification, information seeking, mood management and relational belonging.^{7, 8} Motivational research distinguishes between heterogeneous motives, including pleasure seeking, curiosity, excitement and emotional avoidance, which serve different regulatory and exploratory functions.^{9, 10} Media use driven by avoidance can function as an emotion regulation process, aligning with the self-medication hypothesis. ¹¹ Emotion regulation models further suggest that individuals often engage with media to reduce distress or distract from aversive affective states. ¹² Accordingly, pornography consumption should be understood across two dimensions, such as quantitative exposure and motivational function. Yet relatively few studies have examined how these motivational dimensions relate specifically to suicidal ideation.

Depressive symptoms are considered a key vulnerability factor, given their well-established associations with maladaptive cognitive processing, emotional dysregulation and suicidal ideation.^13–15 Under elevated depressive symptoms, motivationally driven media engagement may acquire different psychological meanings. For example, curiosity-driven exploration may become intertwined with rumination or self-evaluative processing, whereas avoidance-driven use may overlap with maladaptive coping patterns. Conversely, in low-depression contexts, similar motivational engagement may reflect normative exploration without psychological risk implications. These processes may also be understood within broader neurocognitive mechanisms related to reward processing and affect regulation observed in depressive symptomatology.

Suicidal ideation represents an extreme state of psychological distress and is strongly associated with depressive symptoms. ¹⁵ It refers to a distinct cognitive outcome characterised by perceptions of defeat, entrapment and diminished future orientation rather than generalised emotional distress. However, cognitive-affective states are shaped by complex interactions between individual vulnerability and environmental engagement, including media use. Given its severity and clinical significance, it provides a stringent test of whether pornography engagement independently contributes to psychological risk beyond established vulnerability factors. The present study aimed to examine the association between pornography exposure, consumption motives, depressive symptoms and suicidal ideation among young adults. Rather than focusing only on the frequency of use, the study considered both the intensity of pornography exposure and the underlying motives for use, along with the role of depressive symptoms in shaping these associations.

Methods

Procedure and Participants

The study employed a cross-sectional design. The final sample consisted of 541 students (80% male, 20% female; age range: 18–26 years), recruited via online platforms from different Indian universities between November 2025 and February 2026 through convenience sampling. The only inclusion criterion was that participants be at least 18 years of age. Participants were informed about the sensitive nature of the study and that they could discontinue participation at any time. Crisis helplines were provided to all the participants prior to the study. Ethical approval was obtained from the Institutional Review Board (IEC-1072), following Indian Council of Medical Research (ICMR) guidelines and the Declaration of Helsinki. The study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines.

Tools Used

Frequency and Duration of Pornography Use ¹⁶ : Pornography exposure intensity was assessed using self-reported frequency and duration of online pornography use. Participants were first asked to indicate the smallest applicable time unit reflecting their typical pornography use (less than annually, annually, monthly, weekly or daily). They were then prompted to report the specific frequency within the selected time unit (e.g., number of times per week or per day). A weekly exposure estimate was calculated for analytical purposes. Frequency of use was standardised to a weekly metric and multiplied by the reported average session duration to derive total minutes of pornography use per week, representing quantitative exposure intensity.

Pornography Consumption Inventory ¹⁰ : The PCI-15 is a 15-item scale designed to evaluate the motives behind pornography consumption. ¹⁰ The four subscales of the scale are Emotional Avoidance, Sexual Curiosity, Excitement Seeking and Sexual Pleasure. The responses are recorded on a scale of 1 (never like me) to 5 (very often like me). The scale has a good internal reliability (α = 0.83) with subscale α ranging from 0.71 to 0.87 (Emotional Avoidance: α = 0.85, Sexual Curiosity: α = 0.87, Excitement Seeking: α = 0.73 and Sexual Pleasure: α = 0.71).

Beck Scale for Suicidal Ideation ¹⁷ : The Beck Scale for Suicidal Ideation is a 19-item scale designed to assess the frequency and severity of suicidal thoughts. Responses are recorded on a scale of 1–3. ¹⁷ Participants respond to the items that cover domains such as the wish to die, deterrents and preparation for suicide. Higher scores on this scale are related to the severity of the case. The scale has a high internal consistency reliability (Cronbach’s α > 0.85), indicating that the items effectively measure a unified construct of suicidal ideation. The scale demonstrated strong psychometric properties for use in clinical and nonclinical populations.

Depression^{13, 14}: Depressive symptoms were assessed using the eight-item Patient Health Questionnaire (PHQ-8). It is a brief self-report measure derived from the DSM-IV diagnostic criteria for depressive disorders, assesses the frequency of core depressive symptoms over the past 2 weeks, including depressed mood, anhedonia, sleep disturbance, fatigue, appetite changes, feelings of worthlessness, concentration difficulties and psychomotor changes. Items are rated on a four-point scale ranging from 0 (not at all) to 3 (nearly every day). Total scores range from 0 to 24, with higher scores indicating greater depressive symptom severity. Internal consistency reliability for the closely related PHQ-9 has been reported as high (Cronbach’s α = 0.86–0.89) across clinical samples.

Statistical Analyses

All analyses were conducted using Bayesian estimation implemented in Python using PyMC (version 5.28.0) and ArviZ (version 0.22.0) for diagnostics and model comparison. Descriptive statistics and correlations were examined prior to model estimation. Continuous predictors were standardised (z-scores), with age included as a continuous covariate and gender as a binary covariate.

Bayesian Modelling Strategy

Four sequential models were estimated. The Model 0 (M0) model, represented as the baseline vulnerability model, included only depressive symptoms as the predictor of suicidal ideation. Model 1 (M1) added pornography-exposure intensity to the model along with depressive symptoms. Model 2 (M2) extended the model by incorporating motivational dimensions (emotional avoidance, sexual pleasure, sexual curiosity and excitement seeking). Model 3 (M3) included interaction terms between depressive symptoms and pornography motives, as well as the depression × exposure interaction. The Bayesian analysis was conducted and reported in accordance with the Bayesian Analysis Reporting Guidelines. ¹⁸

Distributional Justification and Prior Specification

A Student-t likelihood was used to account for non-normality and potential outliers in suicidal ideation scores. Regression coefficients were assigned normal (0, 0.5) priors, the intercept a normal (0, 1) prior, the residual standard deviation a half-normal (1) prior, and the degrees of freedom an exponential (1/30) prior. These priors were chosen to reflect small-to-moderate psychological effects while allowing flexibility for posterior updating.

Estimation and Convergence

Models were estimated using Hamiltonian Monte Carlo sampling with four independent chains. Convergence was assessed using multiple complementary diagnostics, including Gelman–Rubin statistic (R̂), effective sample size and visual inspection of trace plots. Convergence diagnostics indicated satisfactory convergence (R̂ ≈ 1.00) across all models.

Posterior Predictive Checks

Model adequacy was evaluated using posterior predictive checks by comparing simulated data drawn from posterior distributions with observed suicidal ideation scores. Model fit was considered adequate when posterior predictive simulations closely aligned with the observed distributional characteristics of the suicidal ideation data (Figure 1).

Figure 1. Posterior Predictive Check Demonstrating Model Fit for Suicidal Ideation.

Model Comparison

Predictive performance was assessed using approximate leave-one-out (LOO) cross-validation. Models were compared using expected log predictive density (ELPD_LOO) values, where higher (less negative) values indicate better expected out-of-sample predictive performance.

Results

Model Diagnostics and Fit

Results are presented as posterior estimates with 94% highest density intervals (HDIs), as the range of parameter values with the highest posterior density, containing 94% of the probability mass. The choice of 94% intervals aligns with Bayesian reporting guidelines that prioritise probabilistic interpretation rather than fixed frequentist thresholds. Similarly, regression coefficients (β) are interpreted as the expected change in the outcome variable associated with a one-unit increase in the predictor, with uncertainty quantified through posterior distributions rather than single-point estimates.

Baseline Vulnerability Model (Model 0)

Table 1 presents the baseline vulnerability model, examining whether depressive symptoms, age and gender were associated with suicidal ideation. Depressive symptoms showed a positive association with suicidal ideation (β = 0.58, 94% HDI [0.51, 0.64]), indicating that higher levels of depression were credibly linked to greater suicidal ideation. Gender showed a modest negative association (β = −0.21, 94% HDI [−0.38, −0.05]). Age did not show a meaningful, credible association with suicidal ideation (β = 0.01, 94% HDI [−0.05, 0.08]). The residual standard deviation was σ = 0.75, and the degrees-of-freedom parameter (ν = 12.65) suggested moderately heavy-tailed residuals.

Table 1. Baseline Vulnerability Model Predicting Suicidal Ideation.

Predictor	Posterior Mean ( β )	SD	94% HDI	R̂
Intercept	0.14	0.08	[−0.00, 0.29]	1.00
Age	0.01	0.03	[−0.05, 0.08]	1.00
Depression	0.58	0.04	[0.51, 0.64]	1.00
Gender	−0.21	0.09	[−0.38, −0.05]	1.00

Notes: Model parameters: σ = 0.75; ν = 12.65. Values represent posterior means from Bayesian regression with 94% highest density intervals (HDIs). R̂ values equal to 1.00 indicate satisfactory model convergence. SD: Standard deviation.

Association Between Pornography Exposure and Suicidal Ideation (Model 1)

Table 2 represents Model 1, which examines whether pornography exposure intensity was associated with suicidal ideation after adjusting for depressive symptoms, age and gender. Depressive symptoms showed a positive association with suicidal ideation (β = 0.60, 94% HDI [0.53, 0.66]). Pornography exposure intensity showed a small negative association with suicidal ideation (β = −0.09, 94% HDI [−0.15, −0.03]). Age was not credibly associated with suicidal ideation (β = 0.01, 94% HDI [−0.05, 0.08]). Gender demonstrated a negative association (β = −0.21, 94% HDI [−0.37, −0.05]).

Table 2. Bayesian Regression Model Predicting Suicidal Ideation Including Pornography Exposure.

Predictor	Posterior Mean (β)	SD	94% HDI	R̂
Intercept	0.14	0.08	[−0.01, 0.28]	1.00
Age	0.01	0.03	[−0.05, 0.08]	1.00
Depression	0.60	0.04	[0.53, 0.66]	1.00
Exposure	−0.09	0.04	[−0.15, −0.03]	1.00
Gender	−0.21	0.09	[−0.37, −0.05]	1.00

Notes: Model parameters: σ = 0.74; ν = 13.06. Values represent posterior means from Bayesian regression with 94% highest density intervals (HDIs). R̂ values equal to 1.00 indicate satisfactory convergence. SD: Standard deviation.

Association Between Pornography Motives and Suicidal Ideation

Table 3 presents the Bayesian regression model examining whether pornography use motives were associated with suicidal ideation after accounting for depressive symptoms, exposure intensity, age and gender. Depressive symptoms remained a positive predictor of suicidal ideation (β = 0.60, 94% HDI [0.54, 0.67]). Emotional avoidance showed a positive association with suicidal ideation (β = 0.15, 94% HDI [0.06, 0.23]) among all the motives. Sexual pleasure motives showed a negative association (β = −0.18, 94% HDI [−0.29, −0.08]), suggesting that hedonic engagement was associated with lower suicidal ideation after adjusting for depressive vulnerability. Excitement seeking and sexual curiosity motives did not show any credible association with suicidal ideation. Pornography exposure intensity showed a small negative association (β = −0.10, 94% HDI [−0.16, −0.03]) with suicidal ideation.

Table 3. Bayesian Regression Model Predicting Suicidal Ideation Including Pornography Use Motives.

Predictor	Posterior Mean (β)	SD	94% HDI	R̂̂
Intercept	0.06	0.08	[−0.09, 0.21]	1.00
Emotional avoidance (EA)	0.15	0.05	[0.06, 0.23]	1.00
Excitement seeking (ES)	−0.04	0.06	[−0.15, 0.06]	1.00
Sexual curiosity (SC)	0.03	0.04	[−0.05, 0.11]	1.00
Sexual pleasure (SP)	−0.18	0.05	[−0.29, −0.08]	1.00
Age	0.01	0.03	[−0.05, 0.08]	1.00
Depression	0.60	0.04	[0.54, 0.67]	1.00
Exposure	−0.10	0.03	[−0.16, −0.03]	1.00
Gender	−0.11	0.09	[−0.27, 0.07]	1.00

Notes: Model parameters: σ = 0.73; ν = 12.94. Values represent posterior means from Bayesian regression with 94% highest density intervals (HDIs). R̂ values equal to 1.00 indicate satisfactory model convergence. SD: Standard deviation.

Interaction Effects (Model 3)

The moderation model (Table 4) represents whether depressive symptoms interact with pornography use motives and exposure in predicting suicidal ideation. The interaction between depressive symptoms and sexual curiosity was credibly positive (β = 0.10, 94% HDI [0.02, 0.19]), which indicates that the association between depression and suicidal ideation strengthened at higher levels of sexual curiosity in the moderation model.

Table 4. Bayesian Moderation Model Predicting Suicidal Ideation.

Predictor	Posterior Mean (β)	SD	94% HDI	R̂
Intercept	0.03	0.08	[−0.12, 0.19]	1.00
Emotional avoidance (EA)	0.14	0.05	[0.05, 0.22]	1.00
Excitement seeking (ES)	−0.04	0.06	[−0.15, 0.07]	1.00
Sexual curiosity (SC)	0.02	0.05	[−0.07, 0.10]	1.00
Sexual pleasure (SP)	−0.18	0.06	[−0.28, −0.07]	1.00
Age	0.02	0.03	[−0.05, 0.08]	1.00
Depression	0.60	0.04	[0.53, 0.66]	1.00
Exposure	−0.09	0.04	[−0.16, −0.03]	1.00
Gender	−0.09	0.09	[−0.27, 0.07]	1.00
Depression × EA	0.05	0.05	[−0.04, 0.14]	1.00
Depression × ES	0.02	0.06	[−0.09, 0.14]	1.00
Depression × SC	0.10	0.05	[0.02, 0.19]	1.00
Depression × SP	−0.10	0.06	[−0.21, 0.01]	1.00
Depression × Exposure	−0.02	0.03	[−0.08, 0.05]	1.00

Notes: Model parameters: σ = 0.73; ν = 12.27. Values represent posterior means from Bayesian regression with 94% highest density intervals (HDIs). R̂ values equal to 1.00 indicate satisfactory convergence. SD: Standard deviation.

Model Comparison

Table 5 presents the LOO cross-validation comparison across models, and the comparative model performance is illustrated in Figure 2. The motivational model (M2), which included depressive symptoms, exposure intensity, pornography use motives and covariates, demonstrated the highest ELPD (ELPD-LOO = −648.01), which indicates the best out-of-sample predictive performance. The moderation model (M3) showed a small decrease in predictive performance (∇ELPD = 1.13), suggesting that adding interaction terms did not meaningfully improve predictive accuracy. The findings suggest that pornography use motives enhance predictive performance beyond exposure intensity alone, whereas additional interaction terms provide limited incremental benefit.

Table 5. Leave-one-out (LOO) Cross-validation Model Comparison.

Model	Predictors	ELPD-LOO	SE	∇ELPD	Model Weight
M2	Depression + Exposure + Motives + Covariates	−648.01	10.57	0.00	0.30
M3	M2 + Interaction terms	−649.14	16.28	1.13	0.49
M1	Depression + Exposure + Covariates	−653.67	6.59	5.65	0.00
M0	Depression + Covariates	−655.91	5.68	7.90	0.21

Notes: Higher expected log predictive density (ELPD-LOO) values indicate better out-of-sample predictive performance. ∇ELPD represents the difference relative to the best-performing model (M2). SE: Standard error.

Figure 2. Leave-one-out (LOO) Cross-validation Model Comparison Across Exposure-based, Motivational and Conditional Susceptibility Models.

Discussion

The present study examined the association between pornography use, consumption motives, depressive symptoms and suicidal ideation among young adults. Depressive symptoms emerged as the most robust correlate of suicidal ideation across all models, whereas pornography exposure intensity showed little independent association when psychological vulnerability was considered. The analysis of the model comparison showed that motivational differentiation provided the strongest improvement in predictive performance among all models. These findings collectively suggest that psychological outcomes associated with pornography use are better explained by motivated media selection operating within vulnerability contexts than by exposure intensity alone.

The present findings contribute to the ongoing debates around pornography exposure and its relation to suicidal ideation. The results indicated that weekly pornography exposure did not meaningfully predict suicidal ideation once depression was modelled. The pattern of this result directly challenges the dose-response assumptions, where higher exposure is always related to adverse psychological consequences. These findings align with prior person-centred research, indicating high frequency pornography use does not uniformly translate into psychological distress, challenging simple dose–response-based interpretation. ¹⁹ This pattern is consistent with prior research demonstrating attenuation of exposure effects when psychological variables such as loneliness, anxiety, depression or moral incongruence are controlled.^{4, 6} The results indicate that pornography exposure alone may not function as an independent risk factor for suicidal ideation in this sample.

Another important contribution of this study is highlighting the importance of considering motives for pornography use. Consistent with Uses and Gratifications Theory, pornography engagement is conceptualised as heterogeneous behaviour, shaped by distinct motivational processes. ⁷ Emotional avoidance motives showed positive associations with suicidal ideation, even after accounting for depressive symptoms, consistent with compensatory media use perspectives, suggesting that individuals turn to media to manage distress.^{12, 20} Excitement seeking and pleasure-related components showed small posterior associations, consistent with evidence focusing on variability among pornography users. ²¹ It can be better understood as a normative gratification process rather than a generalised risk factor in suicidal ideation. In contrast, sexual curiosity has emerged as a conditionally relevant factor under heightened depressive vulnerability. The possible explanation might be that individuals with depression have reward processing deficits and anhedonia, which promote increased novelty seeking and stimulation seeking behaviours.^{22, 23} It suggests that curiosity-driven media engagement may represent adaptive compensation rather than maladaptive behaviour. This aligns with the compensatory media use perspectives, emphasising mood-regulation processes.^{12, 20} Overall, these findings indicate that pornography use is not a uniform behaviour, and its psychological implications vary depending on the underlying reasons for use.

The results also indicated the role of depressive symptoms in shaping the relationship between pornography use and suicidal ideation. It suggests that depressive symptoms did not consistently amplify the link between pornography engagement and suicidal ideation. Depressive symptoms represent a dispositional vulnerability in this study, which is strongly associated with suicidal ideation.^13–15 The findings appear to indicate that depression is not only directly related to suicidal ideation but also conditions the association between sexual curiosity and suicidal ideation. Furthermore, the model comparison analyses showed that including multiple interaction terms did not substantially improve predictive performance beyond the motivational model.

The present findings suggest that the polarised narratives of media effects, framing media use as either harmful or harmless, may oversimplify the complex psychological dynamics involved in pornography engagement. Overall, the findings indicate that pornography exposure alone is not meaningfully associated with suicidal ideation after accounting for depressive symptoms. While depression remains the primary factor linked to suicidal ideation, motivational patterns provide additional insight into how pornography use relates to mental health. This may help explain inconsistencies in previous research, where mixed findings have been reported regarding the effects of pornography use on mental health outcomes. The analyses highlight the importance of considering individual differences and patterns of use when examining the association between pornography use and suicidal ideation.

Limitations

Several limitations should be considered while interpreting the results. The current study used a cross-sectional design, which does not allow for causal inferences about the temporal order of pornography exposure, motivational processes, depressive symptoms and suicidal ideation. In addition, this study was based entirely on self-report measures, which are potentially vulnerable to recall bias and socially desirable responding, especially when assessing sensitive variables like pornography use and suicidal ideation. Future studies should examine qualitative aspects of media experiences and contextual meaning-making processes, as pornography use is a heterogeneous phenomenon. The study used online participation as a recruitment method, which could pose issues with generalisability. This study may be biased toward individuals who are comfortable discussing sexuality and mental health issues online. Lastly, the study measured suicidal ideation at the cognitive level and not at the behavioural outcome level. The results of the study should not be interpreted as suggesting that exposure to pornography is a direct cause of suicidal behaviour.

Implications and Future Research

The present study revisited inconsistencies in prior research concerning the psychological implications of pornography use by examining exposure intensity, motivational engagement and depressive susceptibility. Depressive symptoms emerged as the most robust correlate of suicidal ideation, whereas pornography exposure intensity demonstrated negligible independent associations once vulnerability was accounted for. These findings contribute to a growing body of research suggesting that media exposure alone rarely produces uniform psychological outcomes. ²⁴ These findings highlight the importance of assessing emotional distress and coping patterns when evaluating the mental health implications of pornography use.

The absence of a meaningful exposure effect helps clarify inconsistencies in prior pornography research, where associations with psychological distress have varied substantially across studies.^{4, 5, 25} The findings challenge deterministic media effects assumptions and highlight selective media engagement and individual differences. ⁷ Interventions addressing concerns about pornography use may benefit from focusing on emotional well-being and coping processes rather than emphasising abstinence or exposure reduction alone. The current study helps to improve theoretical conceptualisation in pornography studies, which have tended to adopt problematic use or addiction-based models that equate use with distress.^{4, 10} The results suggest that inconsistent findings in previous literature may, in part, be due to a lack of focus on conditional media processes.

Conclusion

It can be concluded from this study that the current results are consistent with the idea that pornography use is dependent on user motivation with psychological context. Rather, it supports a conditional and motivated dependent model of media effects, suggesting that the psychological consequences of pornography use are contingent on the selective engagement processes within the vulnerability context. A differential susceptibility framework for the study of pornography can help to redirect attention from the question of whether media use is detrimental to understanding when, how and to what extent media use is related to psychological risk.

Authors’ Contribution

Chandreyee Roy: Conceived the research idea, designed the study, conducted a review of literature, conducted data analysis, and drafted the manuscript.

Ahmed Sameer: Contributed to study conceptualisation and design, provided supervision, and critically revised the manuscript.

Consent to Participate

Written informed consent was obtained from all participants prior to participation.

Data Availability Statement

The authors are willing to share their analytic methods and study materials with other researchers. Due to the sensitive nature of the variables examined, the data set is not publicly available. However, de-identified data may be made available from the corresponding author upon reasonable request, subject to ethical and institutional guidelines.

Statement of Ethics

The study was approved by the Institutional Review Board (IEC-1072) and conducted in accordance with the Indian Council of Medical Research (ICMR) guidelines and the Declaration of Helsinki.