Introduction

Exposure to and use of pornography during adolescence have undoubtedly become an issue warranting additional research. The deleterious impacts of adolescent pornography engagement suggest that early and chronic exposure may contribute to earlier and riskier sexual behavior (Pathmendra et al., 2023). Nevertheless, the evidence base on pornography habits among adolescents with sexual behavior problems is relatively undeveloped and inconsistent. Further research can identify patterns and distinguish characteristics particularly unique to a subset of youth to elucidate particular risks for treatment and rehabilitative targets.

Pornography Exposure

There is debate in the literature as to what constitutes normative pornography use among adolescents. Rates of pornography exposure in adolescence are varied among studies, with most placing estimates at over 50% of adolescents viewing pornography within the past year (Horner, 2020; Peter & Valkenburg, 2016). Importantly, exposure can occur intentionally (i.e., specifically seeking out pornographic material) or unintentionally (e.g., pop-up ads, unsolicited links, peer exposure, etc.), with unintentional exposure being more common (Flood, 2009; Ybarra & Mitchell, 2005). With over 95% of youth in the USA having access to the Internet (Vogels et al., 2022) and the rise of smartphone use, adolescents have a greater ability to access pornography, whether intentionally or unintentionally.

This ease of access is reflected in typical ages of first pornography exposure, with older youth (≥ 14 years old) more likely to report intentional exposure to pornography compared with younger youth, who disclose more unintentional or accidental exposures (Ybarra & Mitchell, 2005). Little work has delineated differences in pornography use by race/ethnicity, though one study found that Black youth have higher rates of exposure than their white counterparts (West, 2022); it was not specified whether this exposure included both intentional and unintentional access. One consistent finding is that most adolescent users identify as male, and boys report higher frequency of pornography use (i.e., one or more times per week) than adolescent girls (Bőthe et al., 2020; Peter & Valkenburg, 2016). The effect was most prominent for heterosexual boys, though they did not differ significantly in pornography use frequency from sexual and gender minority boys (Bőthe et al., 2020). Still, the literature regarding where we draw the line between normative healthy pornography use vs. problematic or unhealthy use of pornography is scarce, with most definitions relying on the presence of compulsive sexual behaviors, earlier sexual debut, or viewing violent pornography as extremes of problematic use. Of note, the use of pornography does not initiate earlier sexual debut (Matković, et al., 2018), and preference for violent pornography decreases over time for adolescents (Landripet et al., 2019), perhaps indicating the sensation seeking that is a hallmark of this developmental stage.

Relationship Between Pornography Exposure and Sexual Aggression

Whether or not pornography exposure creates risk for sexual violence is a critical question that research has attempted to answer. When looking at outcomes like risky sexual behaviors, studies on samples of adolescents found that pornography exposure was not related to sexually risky behaviors, sexual intercourse prior to age 15, teen pregnancy, or having four or more sexual partners, and only had a slight relationship with poor condom use (Luder et al., 2011). Among emerging adult samples, younger age at time of first exposure to inappropriate sexualized materials was only moderately related to emergence of risky sexual behaviors (Sinković et al., 2013). While age of first exposure to pornography is important to consider, it may co-occur with developmentally inappropriate sexualization and poor sexual boundaries that connote a confluence of potential risk (Ford & Linney, 1995).

When considering the relationship between pornography and sexual aggression, research has predominantly focused on adult men. In a meta-analysis of a representative sample of adults, Malamuth et al. (2000) found connections between frequent pornography use and sexually aggressive behaviors, even after controlling for risk factors such as family violence, attitudes around violence, and hostile masculinity. In the same study, they indicated that aggressive men and those witnessing violent pornography may have a greater risk for sexual aggressive behaviors (Malamuth et al., 2000). This same relationship was found among emerging adults on college campuses, where pornography use had a reliable association with sexual aggression after including model covariates such as attitudes of violence, sexual abuse trauma, or substance use (Carr & VanDeusen, 2004). Research on adult men who have been exposed to aggressive pornography found that men who were “predisposed to be sexually aggressive” had greater sexual fantasies after exposure, suggesting predispositions to sexual aggression may be a key moderating factor (Malamuth, 1981). Importantly, the format and accessibility of pornography has changed over time, with increased access due to technological advancements; results from studies that predate these technologies may yield inconclusive or irrelevant findings.

Recent longitudinal study on adolescents paints a different picture of the relationship between pornography use and sexual aggression. Among youth who reported no or marginal levels of sexual aggression (relative to those who reported more), there was a slight average increase in pornography use over time while there was a decrease in pornography use among peers with moderate sexual aggression (Dawson et al., 2019). However, there still exists evidence that early exposure to pornographic material and recent use of pornography has a longitudinal effect on adolescent boys’ rates of perpetrating sexual harassment (Brown & L’Engle, 2009; Waterman et al., 2022). Other research has observed differences in sexually aggressive outcomes based on viewing violent vs. non-violent pornographic material, with only those who viewed violent pornography displaying increased reports of sexually aggressive behavior (Ybarra et al., 2011).

Sexual and Non-Sexual Delinquency

Youth who engage in sexual and non-sexual delinquency, while sharing a developmental propensity for risky behavior, have been found to have distinct criminogenic needs related to offence aetiology. For instance, while maltreatment is considered a risk factor for any form of delinquency (Seto & Lalumière, 2010; Smith & Thornberry, 1995), youth who engage in sexual delinquency have demonstrably more severe maltreatment histories and family risk factors (Yoder et al., 2018). Experiencing sexual abuse in childhood is of particular aetiological significance for juvenile sexual delinquency (Bosetti, 2024; Knight & Sims-Knight, 2004). Unsurprisingly, these histories place sexually delinquent youth at higher risk of exhibiting trauma symptomology (Morais et al., 2018; Righthand & Welch, 2004). Youth who engage in sexually delinquent offences also exhibit higher rates of social anxiety and discomfort with same-age peers, which has been posited as a risk factor for offending against younger children; comparatively, youth who sexually offend against same-age peers display more diverse offending histories and greatly antisocial attitudes (Seto & Lalumière, 2010). Despite these risk factors, youth who have committed acts of sexual delinquency also have lower rates of sexual recidivism than those who have committed acts of non-sexual delinquency; both groups have equivalent rates of general delinquency (Caldwell, 2016).

Research is inconclusive regarding pornography use differences between youth who engage in sexually offending behaviors and their delinquent counterparts. Sexually abusive youth and youth with problem sexualized behaviors have been found to disclose more frequent and earlier exposures to pornography compared to delinquent youth (Burton et al., 2010; Dillard et al., 2019; Flood, 2009; Seto & Lalumière, 2010). One early comparative analysis between youth who committed sexual offences (against children and peers or adults), youth who commit violent non-sexual crimes, and status offenders suggested youth who commit sexual crimes had earlier exposure (between ages 5 and 8) to pornographic material, with the youth who offend against children being exposed to this material most frequently (Ford & Linney, 1995); however, technological advances making pornography widely accessible to anyone with an Internet connection call the relevance of this finding into question today. One study of sexually reactive children and adolescents found that those who used pornography exhibited more aggressive behaviors (Alexy et al., 2009). However, other research has noted that adolescents who engage in sexual delinquency report less pornography use than youth who engage in non-sexual delinquent behaviors (Driemeyer et al., 2013).

Overall, comparative analyses on pornography use between these groups is scant. Aetiological reviews highlight the role that pornography may play in understanding typological distinctions or as a developmental pathway to adolescent sexual offending (Leversee, 2015), but recognize the evidence base is exploratory. A meta-analysis concludes that pornography or sexually explicit material may distinguish between groups of offenders if material is presented at very young ages (Seto & Lalumière, 2010). Still, most of this work fails to consider the impact of pornography beyond characteristics of age of first exposure, frequency of viewing, and whether the content of the pornography is illegal (i.e., child pornography) or violent. Gathering additional insight into how offending groups may differ in pornography use habits can be a critical prevention and early treatment target with sexually and non-sexually delinquent youth.

Theoretical Integration

The sexual script theory tells us that individuals rely on “scripts” to develop a hermeneutic of sexual behavior and identity, and that these scripts are influenced by individual and social learning components (Simon & Gagnon, 2003). The accessibility of pornography situated within a broader landscape of inadequate sex education may encourage youth to turn to the former to develop their scripts (Rabbitte & Enriquez, 2018; Sun et al., 2016). Although it is not clearly explored in common risk assessment tools for youth with sexually abusive behavior, experts emphasize the importance of assessing pornography use habits when working with sexually abusive youth (Pratt & Fernandes, 2015). Indeed, sexual scripts may partially explain the relationship between pornography consumption and subsequent sexually coercive behavior (Marshall & Miller, 2023). However, the sparse research that has explored the type of pornography used in relation to sexual scripts and illegal sexual behavior has two major limitations: (1) exclusively exploring young adult populations and (2) limiting pornography type to violent/illegal and non-violent/legal (Kingston et al., 2008; Marshall, 2021). Additional research is needed to understand individual pornography use habits (Kingston et al., 2009)—particularly regarding pornography content—as it relates to offending typology, sexual recidivism risk, and generalist or specialist offence histories among high-risk youth.

The Current Study

Pornography content and use is vast and varied, yet much of the research does not examine the specific pornography use habits that may differentiate between sexually and non-sexually offending youth. Further, little is known about how pornography use habits relate to sexual recidivism risk and generalized patterns of delinquent behavior among a high-risk sample of sexually abusive youth. The current study sought to fill these gaps by answering three research questions: (1) Do sexually and non-sexually delinquent youth differ in their pornography use habits? (2) Among sexually delinquent youth, what pornography use habits, if any, are associated with sexual recidivism risk? and (3) Among sexually delinquent youth, what pornography use habits, if any, are associated with overall delinquent behavior? The second question is limited to sexually delinquent youth, as non-sexually delinquent youth do not complete the scale for sexual recidivism risk. In the third question, we also restrict analyses to the sexually delinquent youth to determine whether there are relevant differences in pornography consumption among more generalist delinquent youth who have a sexual offence relative to those who have a more limited profile of sexual delinquency.

Method

Participants

Purposive sampling was employed, where researchers specifically recruited corrections facilities and community-based residential facilities in the state that housed youth with delinquency adjudications. A convenience sample of the full facilities was conducted, whereby youth were presented with the opportunity and were able to decide whether or not to participate in the study. No comparison data are available for facilities or housed youth who declined to participate. Informed consent and assent processes were conducted with youth depending on their age, and parental permission was obtained for minors in the juvenile corrections facilities or waived for minors in the community residential facilities. Prior to providing consent/assent, youth were notified that they were free to ask any questions, refuse to participate, or stop the study at any time without penalty. Mental health staff were available should any of the participants experience distress during the study. Paper-and-pencil surveys were distributed to the youth participants. The survey took approximately 60 min to complete. Community-based youth completed surveys individually, whereas youth in juvenile corrections facilities were split into small groups of 10–15 to complete the survey. Youth were provided with either a pizza lunch or a gift card as an incentive, depending on agency policy.

Survey data were collected from male youth (N = 200) ages 12–23 who were housed in either community residential (n = 54) or juvenile corrections facilities (n = 146) in a Western state. Average age of participants was 17.19 years old. The youth were primarily White (40%), followed by Hispanic (35%), Black (20%), and other race (9%); race categories were not mutually exclusive so that youth could disclose being of mixed racial identities, resulting in the total exceeding 100%. See Table 1 for full demographic characteristics.

Table 1 Demographic characteristics of full sample and offending subsamples
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Measures

Pornography Use Habits

To assess pornography use and attitudes, we used a Pornography Scale that has been used with samples of incarcerated youth and youth who committed acts of sexual delinquency (Burton et al., 2010). The psychometric properties of the Pornography Scale have not been previously assessed. The measure includes nine categories of questions that assess frequency of viewing pornography, mode of viewing, beliefs that pornography is realistic, type of pornography, location of viewing, other individuals partaking in pornography viewing, and sexual arousal patterns when viewing. The items reflect general viewing habits rather than asking youth to consider their patterns over a specified time period.

Separate models were run based on four subcategories of the pornography measure. The Sexually Exciting Content subcategory included 16 items related to the acts being performed in the pornography, with participants responding to how much they were sexually excited by the particular act on a 5-point Likert scale (1 = Not at all to 5 = Always). The Sexually Exciting Content subcategory showed high internal consistency with Cronbach’s alpha (α = .89). The Selection Criteria subcategory included nine items related to how participants would select the pornography they chose to view (e.g., attractiveness of actors, title of video, etc.), and rated how often those criteria were used in selecting pornography to view (1 = Never to 7 = Every Time). The Selection Criteria subcategory showed high internal consistency with Cronbach’s alpha (α = .85). The subcategory Context of Use included seven items about the setting in which youth would view pornography, including how often they watch viewing alone or with partners or peers, listen to the audio, masturbate, or reach orgasm on a 7-point Likert scale (1 = Never to 7 = Every Time). The Context of Use subcategory showed high internal consistency with Cronbach’s alpha (α = .81). Finally, the Beliefs and Usage subcategory included four items measuring how often they use pornography (0 = Never to 9 = More than 3 times a day), how much time they spend viewing pornography (1 = Less than 6 h per year to 9 = More than 3 h per day), and whether they believe pornography is realistic or fantasy (1 = Strongly Disagree to 4 = Strongly Agree). The Beliefs and Usage subcategory showed moderate internal consistency with Cronbach’s alpha (α = .66). Within each of the four models, items were entered as independent predictors to analyse their individual contributions to explaining variance in outcome measures, rather than being treated as latent subscales.

Risk for Sexual Recidivism

To assess risk for sexual recidivism, the Juvenile Sexual Offense Recidivism Risk Assessment Tool-II (JSORRAT-II; Epperson et al., 2009) was used. The measure includes 12 items measuring risk for sexual recidivism that were slightly adapted for self-report. These adaptations included minor changes to the preface of the question, with example items such as, “How many times have you been charged with sexual offences?” The response items are varied by question with some items including dichotomous responses (i.e., Do you have a history of special education placement? [0 = no; 1 = yes]) while others have four response options (i.e., Length of sexual offending history based on the time between the charge date for the first sexual offence and the charge date for the most recent sexual offence [0 = zero time (only one charge date); 1 = 1 day to 5.99 months; 2 = 6.00 to 11.99 months; 3 = 12 or more months]). The sum score was used to indicate greater risk with higher scores.

Delinquent behavior

The Self-Report Delinquency Scale (SRD; Elliott & Ageton, 1980; Elliott & Huizinga, 1989) was used to measure non-sexual delinquency prior to their arrest. This measure included 33 items with example items including, “Before I was arrested, I stole money or other things from my parents or other members of my family”. Responses were on a scale ranging from 0 = Did Not Do to 6 = 2–3 Times a Day. All items were presented in their original wording with no items excluded. The composite score was constructed by averaging the response for all items. The mean SRD score was used as a continuous outcome in analyses.

Offending Category

The offending category was measured by asking youth whether they committed a sexual crime that could get them in trouble with the law (0 = No; 1 = Yes) or whether they committed a non-sexual crime that could get them in trouble with the law (0 = No; 1 = Yes). In this sample, 34% of youth endorsed committing a sexual offence (n = 68), with 66% of remaining youth reporting the exclusive commission of non-sexual offences (n = 132).

Analytic Strategy

Binary logistic regression and multiple linear regression were used for analyses. All models included age and race/ethnicity (White/Black/Hispanic/Other) as control variables; these variables are not used for comparative analyses; rather, they are included to determine the relative effects of independent variables on dependent variables net the influence of demographic characteristics. For research question 1, binary logistic regression was used, with offence type (sexual/non-sexual) as the dependent variable to explore whether group membership was indicated by specific pornography use habits. For research questions 2 and 3, only youth who disclosed a sexual offence were included, and multiple linear regression was used. In the model for research question 2, composite sum scores on the JSORRAT-II (Epperson et al., 2009) were regressed onto pornography use habit variables to determine whether sexual recidivism risk was related to any habits. Finally, for research question 3, mean SRD (Elliott & Ageton, 1980) scores were regressed onto pornography use habit variables to see if viewing was related to overall delinquency among youth who disclosed a sexual offence.

Results

Aim 1

The first set of models found that youth who committed sexual and non-sexual crimes did not significantly differ in their frequency of pornography use or the selection criteria they used to choose pornography. However, youth who reported that “for the most part, I believe pornography is realistic and who listen to the audio in pornographic videos” had lower odds of reporting that they had committed a sexual offence than a non-sexual offence (OR = .62, p = .031) and (OR = .73, p = .032), respectively. Youth who disclosed being sexually excited by pornography that featured cartoons and sex with a man dressed as a much younger boy were more likely to report having committed a sexual offence (OR = 3.89, p = .014) and (OR = 70.30, p = .022), while those who disclosed being sexually excited by pornography featuring sex while someone is “sleeping” were less likely to report having committed a sexual offence (OR = .10, p = .012). See Table 2 for complete results.

Table 2 Aim 1: logistic models with offending outcome (sexual vs. non-sexual delinquency)
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Aim 2: JSORRAT-II

The second set of models only included youth who reported commission of a sexual offence (n = 68). None of the pornography beliefs or selection criteria characteristics was associated with JSORRAT scores, though frequency and setting were related. Time spent viewing pornography was significantly associated with elevated sexual recidivism risk for sexually delinquent youth (β = .378, t(59) = 2.14, p = .037). As for the setting of pornography use, those youth who reported regularly having an orgasm while watching pornography showed higher sexual recidivism risk (β = .497, t(60) = 2.16, p = .035). See Table 3 for all results.

Table 3 Aim 2: multiple linear regression on JSORRAT sum scores
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Aim 3: Self-Report Delinquency Scale

Among youth who disclosed a sexual offence (n = 68), frequency of pornography use and beliefs about pornography were not associated with delinquency scores. Results showed that how often sexually delinquent youth watch pornography with a sexual partner (β = .322, t(48) = 2.38, p = .021) and selected pornography based on the sexual act performed corresponded with significant increases in SRD score (β = .402, t(45) = 2.26, p = .028). Among pornography contents that youth found sexually exciting, an increase in how exciting they found sexual pain for yourself (β = .514, t(38) = 3.20, p = .003), showing your body to others without their permission (β = .564, t(38) = 3.24, p = .002), and a woman being forced (β = .377, t(38) = 2.27, p = .029) was significantly associated with higher SRD scores, whereas an increase in how exciting they found “barely legal” males (β = -.413, t(38) = -2.40, p = .020) and sex while someone is “sleeping” (β = -.458, t(38) = -2.47, p = .018) coincided with decreases in SRD score. See Table 4 for full results of SRD regressions.

Table 4 Aim 3: multiple linear regression on self-report delinquency scores
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Discussion

Aim 1

In the first model comparing sexual delinquent and non-sexually delinquent youth, there were a few differentiating factors. Specifically, non-sexually delinquent youth were more likely to report that they believe pornography is representative of real sexual encounters, as well as to listen to the audio of pornography while they were watching. It could be the case that these youth believed that the content they were viewing was realistic due to its live-action nature or the widespread use of pornography as a means of accessing sexual information in the absence of comprehensive sex education in school settings (Attwood et al., 2018; Brown & L’Engle, 2009; Rabbitte & Enriquez, 2018). On the other hand, the non-sexually delinquent youth may have limited sexual encounters in their lives and lack a frame of reference for whether pornography is a realistic representation of a sexual encounter. Indeed, among general population adolescents, the frequency with which they view pornography is positively associated with how realistic they believe pornography to be (Gunnoo & Powell, 2023); however, this association is not found consistently across samples and geography (Wright & Štulhofer, 2019). Still, it is noteworthy that the perceived realism of pornography helps to explain the effect of pornography use on risky—though not illegal—sexual behaviors among adolescents (Wright et al., 2023), an effect that may be observed alongside other broad forms of risky behaviors such as delinquency. One longitudinal study found that young men who believe pornography to be realistic develop strong sexual scripts for subsequent risky sexual behavior (Krahé et al., 2025). This heuristic can also influence the acts that young men try to re-enact or request of their partners during sexual encounters (Sun et al., 2016). Aligning with these findings, Osuna and Holt (2024) found that men who commit sex offences do not widely rely on pornography as scripts of their offences.

Interestingly, youth who engaged in sexual delinquency did not share this belief that pornography was realistic, and in fact sought out forms of pornography less likely to be perceived as representative of reality. Sexually delinquent youth were much more likely to report watching cartoon pornography compared to their non-sexually delinquent counterparts, which removes perceptions of live-action realism. When linked with previous findings on frequency and interpretations of realism, we see a trend of sexually offending youth consuming less pornography (Driemeyer et al., 2013; Gunnoo & Powell, 2023). They were also more likely to report seeking out pornography with a male dressed as a younger boy. Prior work has found that youth who engage in sexual delinquency experience significantly higher levels of social anxiety than their delinquent peers, which is aetiologically significant for offences against younger children (Seto & Lalumiere, 2010). These interactions may stem from curiosity and discomfort exploring sex with same-age peers. Both cartoons and younger individuals may be perceived as less threatening than live-action portrayals of sexual encounters with same-age peers or partners.

While there is some literature identifying atypical sexual interests as a risk factor for juvenile sexual delinquency (Driemeyer at el., 2013; Pullman & Seto, 2012), those interests are typically defined as paedophilic in nature (e.g., consuming child pornography) or as reflecting a paraphilic disorder. Notably, paraphilic disorders—especially paedophilia—are diagnosed with individuals ages 16 years and older, and there is a low level of evidence on which to base the diagnosis of paraphilias in children and adolescents (American Psychological Association, 2013; Thibaut et al., 2016). While consuming pornography featuring a male dressed as a younger boy could be considered atypical, there is a critical distinction between an age play kink (where adults dress up or pretend to be an age different from their own) and paedophilia (where someone exhibits a true sexual attraction to prepubescent children) (Blanchard et al., 2009; Taormino, 2012); the same is true for the difference between consuming pornography depicting age play—as captured by the measure in this study—and consuming true child pornography—which was not measured. Further, youth may have interpreted the item as pertaining to pornography showing a grown man dressed as an adolescent, potentially signifying that they were interested in pornography that depicts a similar-age peer as opposed to a smaller child. Clarification of these terms and qualitative work with youth to explain their thought process and understanding of the items are crucial for further research.

Aim 2

When looking at the risk of sexual recidivism among sexually delinquent youth, only two pornography use habits were related. The habits were time spent viewing pornography and orgasming while watching pornography. Though there is no scientific basis connecting pornography use generally with juvenile sexual offending, there is a noted connection between frequency of pornography use as a marker of sexual preoccupation driving the escalation of sexual offending behaviors (Pratt & Fernandes, 2015). Orgasm and sexual gratification while consuming pornography may also act as positive reinforcement for pornography use, leading to additional time spent watching or encouraging interest in particular content (Pizzol et al., 2016). Though both factors have been used as indicators of sexual preoccupation, it has primarily been with adult samples (Kingston & Bradford, 2013), as pornography consumption and masturbation to orgasm while engaging with sexual stimuli are considered normative and integral to male adolescent sexual development (Galatzer-Levy, 2012). Nevertheless, identification of sexual preoccupation or compulsive pornography consumption and masturbation may be useful targets for prevention among community-based youth or early intervention among youth who have previously engaged in sexual delinquency.

None of the pornography beliefs or selection criteria was related to recidivism risk assessment scores. There are several potential explanations for this finding. First, youth who engage in sexual delinquency are already at low risk for sexual recidivism, with about 3% going on to commit an additional sexual offence and the rate actively declining (Caldwell, 2010, 2016). Due to the low sexual recidivism risk, many risk assessment measures have had their reliability with this population called into question (Caldwell et al., 2008). Further, while research has proposed that adult men who are already predisposed to engage in sexual violence show the strongest effect of pornography exposure (Seto et al., 2001), this finding has not been replicated with youth. Youth and adults have distinct aetiological pathways to sexual offending, and different risk levels for sexual recidivism, so this observed effect with adults may not hold true with a youth population. A final possibility is that, since youth do not have access to pornographic material in the residential community or corrections facilities, any direct effect of pornography use on recidivism risk is not observed when surveying during their incarceration.

Aim 3

In the final model, the relationship between pornography use habits and level of general delinquency was explored among sexually delinquent youth. Pornography content representing violent and forceful sexual interactions was associated with higher levels of general delinquent behavior, suggesting that there may be an unmeasured factor supporting violent and delinquent attitudes more broadly among generalist youth. This finding is in line with previous structural work indicating that exposure to violence and exposure to pornography are predictive of total non-sexual delinquency among youth who have committed sexual delinquency, but are not related to the total number of sexual victims (Hunter et al., 2010). The study suggests that psycho-social deficiencies (e.g., low self-esteem, peer rejection, etc.) and psychopathic attitudes (e.g., impulsivity, antisocial traits, etc.) have a mediating effect on the relationship between pornography consumption and total non-sexual delinquency for juvenile sex offenders (Hunter et al., 2010), indicating these as traits as avenues for continued investigation among incarcerated youth. Meanwhile, preferences for pornography content and characteristics that were non-consensual in nature but not overtly physically violent were associated with lower overall delinquency scores. This fits with the aforementioned hypothesis that youth who engage more exclusively in sexual delinquency may be doing so out of social anxiety rather than a propensity for violence or aggression.

Limitations

The present study had several limitations that must be acknowledged. First, the data collection was cross-sectional, with surveys being completed by youth at a single time point. Thus, no causal relationships can be inferred. While the authors accounted for the temporal order of the variables in analyses, there are significant limitations in the ability to conclude directionality of variable relationships. For instance, pornography consumption may have a reciprocal relationship with sexual risk, where underlying sexual compulsivity influences greater pornography consumption, indicating reverse causality. Moreover, sexually abusive behavior may co-occur with or develop alongside pornography consumption habits. Longitudinal research that starts with first exposure to pornography can overcome this limitation by establishing a clear temporal connection between initial pornography use and subsequent sexual delinquency. Data were collected in a single Western state; thus, youth do not demographically represent the US population of justice-involved youth at large, and generalizability of findings to other regions may be limited. Agencies self-selected into participating, which may further bias the results.

There are also concerns with measurement, specifically with the SRD and JSORRAT-II, which have variable question structures and response options that may not be intuitive to participants. Moreover, the pornography habits measure was far from comprehensive given the vast array of content and thus explored only the most common categories. For youth in residential or correctional facilities, their responses to the pornography items may have been skewed by their restricted access to pornography in the facility. Despite the pornography questions being structured in a way that does not necessitate current use, but rather, general questions pertaining to any use, the youth still may have underreported on these items. Finally, the survey was self-report and retrospective in nature, introducing the possibility of social desirability and recall biases. This may be particularly salient for responses to pornography use items, as youth may have felt discomfort sharing their engagement with a stigmatized practice.

Implications

Research

The findings of this study open several lines of inquiry for further exploration. First, a longitudinal study should be pursued to determine whether the identified characteristics can predictively differentiate youth who engage in sexual and non-sexual delinquency. Pornography preferences should be examined in relation to early content exposures to better understand how usage either remains stagnant or evolves over time. Mixed-methods work should be conducted to better understand the relevance of selection criteria and usage habits within the context of youths’ broader sexual beliefs and behaviors. Future quantitative work should triangulate multiple sources of assessment (e.g., self-report survey, administrative data, intake assessment, parental report, Internet search history, etc.) to overcome concerns of bias that accompany youth self-report as a standalone data source around the sensitive topics of pornography use and delinquent behavior. Research should be conducted with a diverse, representative sample, as there may be differences in pornography consumption and habits based on relevant demographics. Girls, transgender, and non-binary youth should also be included in future samples to better understand what role, if any, pornography plays in behaviors and scripts of appropriate sexual conduct.

Practice

This study has implications for practitioners, providing some insight into the pornography habits that may be distinctive for sexually delinquent adolescents and how those habits relate to overall delinquent behavior for the population. Pornography literacy programmes (PLPs) have been lauded for their role in preventing problematic pornography use (PPU). Studies have found support for PLPs across parents, teachers, and adolescents (Baker, 2016; Davis et al., 2021), and that parents prefer open dialogue with their children about pornography compared with using digital blocks for websites (Davis et al., 2021). Caregivers can be effective sources of education around pornography (Mitchell et al., 2014; Rasumussen et al., 2015), but are hesitant to open dialogues with their children about pornography and sex due to their own embarrassment and discomfort with the topics (Malacane & Beckmeyer, 2016; Rasmussen et al., 2015). This may be why—despite parents being preferred sources of information—few youth discuss pornography with their parents (Widman et al., 2021). Teachers and agency staff face similar challenges overcoming the barriers of discomfort, though interventions have been shown to increase staff efficacy at delivering information and education regarding pornography use (Maas et al., 2022).

PLPs are most effective when they are developmentally informed and appropriate, rely on trained staff, are grounded in broad-scale comprehensive sex education, have a focus on enhancing critical thinking skills, instil realistic views of sex, involve participatory learning activities, are delivered by trained and empowered staff, and adopt whole agency approaches (Crabbe & Flood, 2021; Dawson et al., 2020; Testa et al., 2023). Implementation of staff training and PLPs as part of comprehensive sex education in incarceration institutions may be one of the best means of preventing future PPU and subsequent violent behaviors. Web-based approaches have been effective sources of PLPs and comprehensive sex education for adolescents (Scull et al., 2021), ensuring delivery of PLPs by trained educators and overcoming issues of accessibility in secure settings. Even in the absence of sex education and PLPs, one of the biggest protective factors against early sexual debut and accessing pornographic material is the amount of time adolescents spend with their families of origin (Astle et al., 2020). Interventions and support services to increase contact with families and caregivers, lessening restrictions on visitation in secure settings, and inviting families to participate in PLPs alongside their incarcerated children are recommended to mitigate the effects of PPU and encourage healthy attitudes and behaviors when youth return to their communities.