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. 2024 Oct 25;11(11):1292. doi: 10.3390/children11111292

The Impact of Parental Monitoring on Exposure to Multiple Substances and Bullying in Croatian Students

Maja Valentic 1,*, Luka Simetin 2, Dijana Mayer 1,3, Filip Simetin 2, Ivana Pavic 1
Editors: Elitsa Dimitrova, Apolinaras Zaborskis
PMCID: PMC11593264  PMID: 39594867

Abstract

Background/Objectives: Adolescence is a critical period for experimenting with a wide range of risky behaviors, which are often influenced by family dynamics, including parental monitoring. This study aims to analyze the patterns of exposure to multiple substances and bullying among Croatian students by age and gender in 2022 and examine the association of exposure to multiple substances and bullying with maternal and paternal monitoring. Methods: The data were obtained from the 2022 Health Behaviour in School-aged Children (HBSC) study conducted in Croatia among students aged 11, 13, and 15. Two multinomial logistic regressions were performed separately by age and gender. Results: Exposure to multiple instances of bullying is more common among boys than girls in all three age groups, while exposure to multiple substances varies depending on age. The analysis revealed the strong protective effect of maternal monitoring against both substance use and bullying. Paternal monitoring showed less consistent effects but still indicated that lower paternal monitoring was associated with higher exposure to multiple substances and bullying, particularly at age 13. Conclusions: The patterns of multiple substance use and bullying vary by gender and age, emphasizing the need for tailored intervention strategies. Programs that strengthen parental monitoring, particularly maternal monitoring, should be prioritized.

Keywords: parental monitoring, substance use, bullying, students

1. Introduction

Adolescence is a dynamic and critical development period characterized by many social, physical, physiological, and psychological changes [1]. Its defining characteristics include independence from parents and family, valuing friendships and peer groups, exploring one’s identity, and experimenting with a wide range of behaviors, some of which are risky and inappropriate [1].

Adolescent multiple substance use is a significant and growing problem today, as it is linked to poor mental health as well as negative educational and social outcomes [2,3,4]. The early initiation of substance use and the use of multiple substances are strong indicators of future substance use problems and disorders [4]. Patterns of using multiple substances may identify different groups of adolescents with unique risk factors and future outlooks [2].

School bullying is a major social problem affecting children and adolescents worldwide [5]. It involves repeated negative actions over a period of time and can be direct, such as physical and verbal violence, or indirect, such as social exclusion [6]. Bullying is characterized by harmful intent and an imbalance of power that makes it hard for the victim to defend themselves [6]. A relatively new form of bullying is called cyberbullying, i.e., bullying via mobile phone or the Internet [7]. Many children who are involved in bullying, either as perpetrators or as victims, run the risk of facing psychological difficulties later in life [6,7,8,9,10,11,12,13].

Family dynamics is one of the key factors for shaping behaviors such as adolescent substance use and bullying [14,15,16]. Throughout adolescence, parents play a critical role in promoting healthy development, partly through parental monitoring [17,18,19,20]. Parental monitoring is a set of parenting behaviors that involves attention to and tracking youth’s whereabouts, activities, and friendships [21].

The existing research on adolescent substance use emphasizes the importance of parental monitoring as a protective factor in its prevention [20,22,23,24,25,26]. Some studies suggest that parental monitoring has a protective effect against bullying and its negative outcomes, while other studies indicate it may be unrelated or even positively related to bullying perpetration and victimization [18,27,28,29,30,31]. Moreover, some studies suggest that maternal monitoring or maternal knowledge reduces adolescent substance use and bullying behaviors, while other studies highlight the additional protective effects of paternal knowledge and father–youth connectedness [32,33,34,35,36].

However, research that focuses on multiple substance use as well as exposure to multiple instances of bullying and its association with parental monitoring is still missing. Besides, there is a lack of research on these dynamics in Croatia, where cultural nuances may affect these associations despite their recognized importance.

This study aims to analyze the patterns of exposure to multiple substances and multiple instances of bullying among Croatian students by age and gender in 2022. Additionally, we aim to investigate the association of exposure to multiple substances as well as multiple instances of bullying with parental monitoring, both maternal and paternal. Exposure to multiple substances and bullying reveals particularly vulnerable students, which is why it is especially important to understand the characteristics of their parents to form appropriate preventive activities.

2. Materials and Methods

2.1. Sample

The data used for analysis were obtained from the 2022 Health Behaviour in School-aged Children (HBSC) study conducted in Croatia. The HBSC study is a WHO cross-sectional study that takes place every four years in various countries across Europe and North America. It comprises data from students aged 11, 13, and 15, and follows an internationally standardized protocol.

The Croatian sample was selected based on the official list of schools provided by the Ministry of Science and Education. The sampling unit used was the school class. School classes were chosen randomly at the national level and, for 15-year-olds, they were categorized by the type of high school. The sample consisted of 5338 students, with 51.69% girls (2759) and 48.31% boys (2579), and had a response rate of 64.60%. The sample included 1763 students aged 11 (average age 11.07), 1940 aged 13 (average age 12.96), and 1635 aged 15 (average age 14.99).

2.2. Measures

An internationally standardized questionnaire, translated into Croatian by back-translation, was used as the research instrument. Data collection occurred in the spring of 2022, between March and May. The survey was carried out anonymously and voluntarily, with passive parental consent. The online questionnaire was self-administered by students in the classroom under the supervision of a teacher, using the LimeSurvey online platform (https://community.limesurvey.org/; Version 3.28.26+220829).

2.2.1. Exposure to Multiple Substances

We created a new exposure to multiple substances variable, which we used as the dependent variable. We composed exposure to multiple substances from the following four variables at the age of 11 and 13: got drunk, smoked cigarettes, tried e-cigarettes, and drank energy drinks at least once in a lifetime. The question about lifetime cannabis use was posed only to pupils at age 15. This newly created exposure to multiple substance variable was split into three categories where those who used no substance (no exposure) were compared with those who used 1 or 2 substances (low exposure) and those who used 3 or 4 substances at the age of 11 and 13 and 3–5 substances at the age of 15 (high exposure).

Lifetime drunkenness was assessed with the question “Have you ever drunk so much alcohol that you were really drunk in life?” Response options were on a five-point scale ranging from “never” to “more than 10 times (or more)”.

Lifetime cigarette smoking was measured with the question “How many days did you smoke cigarettes in life?”. Response options were on a seven-point scale ranging from “never” to “30 (or more) days”.

Lifetime e-cigarette use was evaluated using the question “How many days have you used electronic cigarettes (e.g., e-cigarettes, Wiip, e-hookah)? Please do not include products that ‘heat, not burn’ (e.g., IQOS, Glo, TEEPS)”. Response options were on a seven-point scale ranging from “never” to “30 (or more) days”.

Lifetime energy drinks use was evaluated using the question “Currently, how often do you drink energy drinks (e.g., Red Bull, Burn, Monster)? Also count taking small amounts”. Response options were on a five-point scale ranging from “never” to “every day”.

Lifetime cannabis use was evaluated using the question “Have you ever used cannabis?”. Response options were on a seven-point scale ranging from “never” to “30 (or more) days”.

2.2.2. Exposure to Multiple Instances of Bullying

We formed a new exposure to multiple instances of bullying variable, which we used as the dependent variable. We determined exposure to multiple instances of bullying from the following four variables: bullying perpetration, bullying victimization, cyberbullying perpetration, and cyberbullying victimization. This newly created exposure to multiple instances of bullying variable was split into three categories, where the category “no exposure” includes those who did not participate in bullying, including cyberbullying, neither as victims nor as perpetrators. The category of “low exposure” includes the response “once or twice” regarding 1 to 4 bullying variables combined with none, and 1 response of “2 or 3 times a month”, combined with 3 negative answers. All other responses to at least one of four bullying variables (“2 or 3 times a month” in more than one bullying variable, “about once a week”, and “several times a week”) are classified in the “high exposure” category.

Bullying perpetration was assessed with the question “How often have you taken part in bullying another student(s) at school in the past couple of months?”.

Bullying victimization was measured with the question “How often have you been bullied at school in the past couple of months?”.

Cyberbullying perpetration was evaluated using the question “In the past couple of months how often have you taken part in cyberbullying (e.g., sent mean instant messages, email or text messages, wall postings, created a website making fun of someone, posted unflattering or inappropriate pictures without permission and posted them online or sent them to others)?”.

Cyberbullying victimization was assessed with the question “In the past couple of months how often have you been cyberbullied (i.e., someone sent mean instant messages, email or text messages, wall postings, created a website making fun of me or someone took unflattering or inappropriate pictures of me without permission and posted them online)?”.

Response options for all four bullying variables were not once, once or twice, two or three times a month, about once a week, and several times a week.

2.2.3. Parental Monitoring

We assessed parental monitoring with two created variables: maternal and paternal monitoring. The parental monitoring variables were independent in the analysis. We derived these two new variables from the answer to an identical question about mother and father with the following sub-questions: “How much does your mother/father know about...? who your friends are, how you spend your money, where you are after school, where do you go at night, and what you do on the internet”. Response options were as follows: mother/father “knows a lot”, “knows a little”, “doesn’t know anything”, and “doesn’t have or doesn’t see mother/father”. These newly created parental monitoring variables were split into three categories. The category “high monitoring” included the responses “knows a lot” to all five sub-questions and combinations of one response “knows a little” with four responses “knows a lot”. Other responses fell into categories “low mother/father monitoring” or “no monitoring” (“don’t have or don’t see mother/father”).

2.3. Statistical Analysis

Descriptive statistics was used to present the sample characteristics. Gender differences in dependent and independent variables were examined and tested using Pearson’s chi-squared test. Two multinomial logistic regressions were performed separately for boys and girls in three age categories: 11, 13, and 15 years. First, a multinomial logistic regression was performed with exposure to multiple substances as a dependent variable and two mutually independent variables/factors: monitored by the mother and monitored by the father. Second, a multinomial logistic regression was performed with exposure to multiple instances of bullying as the dependent variable and two mutually independent variables/factors: monitored by the mother and monitored by the father.

The results of the logistic regression were presented as odds ratios with 95% confidence intervals.

The statistical significance level was set at p < 0.05.

IBM SPSS version 28 (IBM, Armonk, NY, USA) was used for conducting the statistical analyses.

3. Results

The results section is organized as follows: first, we present the findings related to exposure to multiple substances by age and gender, followed by the analysis of exposure to multiple instances of bullying. Lastly, we examine the associations between parental monitoring and exposure to both substances and bullying.

As presented in Table 1, high exposure to multiple substances was more prevalent among boys at age 11 (6.6%) compared to girls (4.6%), with a significant gender difference (p < 0.001). By age 15, girls started to show higher exposure than boys (40.5% vs. 34.2%, p = 0.025). Substance use increased with age for both genders, with girls surpassing boys in cigarette (42.7% vs. 34.8%) and e-cigarette (42.7% vs. 34.8%) use at age 15 (p < 0.001 for both). Drunkenness was more common among boys at age 11 (11.9% vs. 7.1%, p = 0.001), but by age 15, the rates were almost identical for both genders (45.3% vs. 45.9%). Similarly, boys at age 11 were more likely to consume energy drinks (33.4% vs. 25.6%, p < 0.001), but by age 15, the gender gap narrowed (64.4% of boys and 61.1% of girls). For cannabis use, no significant gender differences were found at age 15 (15.9% for boys and 16.5% for girls).

Table 1.

Exposure to multiple substances by age and gender.

Exposure to Multiple
Substances		 Age 11 Age 13 Age 15
Boys Girls χ2 p Boys Girls χ2 p Boys Girls χ2 p
Exposure to multiple substances
High exposure 58 41 16 <0.001 160 180 1.1 0.580 256 359 7.5 0.025
6.6% 4.6% 16.7% 18.3% 34.2% 40.5%
Low exposure 279 223 392 385 317 326
32.0% 25.1% 40.9% 39.2% 42.4% 36.8%
No exposure 536 626 406 416 175 202
61.4% 70.3% 42.4% 42.4% 23.4% 22.8%
Lifetime drunkenness
At least once 104 63 12 0.001 202 185 1.5 0.220 339 407 0.1 0.820
11.9% 7.1% 21.1% 18.9% 45.3% 45.9%
Never 769 827 8 796 409 480
88.1% 92.9% 78.9% 81.1% 54.7% 54.1%
Lifetime cigarette smoking
At least once 72 55 2.8 0.093 171 197 1.6 0.21 260 379 10.8 0.001
8.2% 6.2% 17.8% 20.1% 34.8% 42.7%
Never 801 835 787 784 488 508
91.8% 93.8% 82.2% 79.9% 65.2% 57.3%
Lifetime e-cigarette use
At least once 75 63 1.4 0.237 204 239 2.6 0.108 265 410 19.5 <0.001
8.6% 7.1% 21.3% 24.4% 35.4% 46.2%
Never 798 827 754 742 483 477
91.4% 92.9% 78.7% 75.6% 64.6% 53.8%
Lifetime energy drink use
At least once 292 228 12.9 <0.001 490 497 0.1 0.831 482 542 1.9 0.165
33.4% 25.6% 51.1% 50.7% 64.4% 61.1%
Never 581 662 468 484 266 345
66.6% 74.4% 48.9% 49.3% 35.6% 38.9%
Lifetime cannabis use
At least once 119 146 0.1 0.763
15.9% 16.5%
Never 629 741
84.1% 83.5%
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As presented in Table 2, exposure to bullying was more frequent among boys at age 11 (9.4% vs. 6.4%, p < 0.001), and boys remained more involved in bullying perpetration and victimization across all age groups. At age 15, boys were more likely to be victims of bullying 2–3 times per month (10.8% vs. 5.3%, p < 0.002), and cyberbullying perpetration was higher among boys compared to girls at age 15 as well (7.3% vs. 1.8%, p < 0.001).

Table 2.

Exposure to multiple instances of bullying by age and gender.

Exposure to Multiple Instances of Bullying Age 11 Age 13 Age 15
Boys Girls χ2 p Boys Girls χ2 p Boys Girls χ2 p
Exposure to multiple instances of bullying
High exposure 69 49 23.5 <0.001 95 72 23 <0.001 73 31 40 <0.001
9.4% 6.4% 11.7% 8.2% 11.3% 3.8%
Low exposure 307 249 377 338 216 230
41.9% 32.5% 46.5% 38.7% 33.4% 28.4%
No exposure 357 467 338 464 357 549
48.7% 61.0% 41.7% 53.1% 55.3% 67.8%
Bullying perpetration
Never 649 713 13.7 0.008 675 778 19 0.001 558 761 42 <0.001
81.5% 87.3% 77.5% 85.4% 81.7% 91.8%
Once or twice per month 107 68 121 84 58 43
13.4% 8.3% 13.9% 9.2% 8.5% 5.2%
2–3 times per month or more 40 36 75 49 67 25
5.0% 4.4% 8.6% 5.4% 9.8% 3.0%
Bullying victimization
Never 613 634 1.6 0.816 656 656 5.7 0.219 555 707 17 0.002
77.1% 77.8% 75.7% 72.2% 81.3% 84.9%
Once or twice per month 99 101 107 143 54 82
12.5% 12.4% 12.3% 15.7% 7.9% 9.8%
2–3 times per month or more 83 80 104 109 74 44
10.4% 9.8% 12.0% 12.0% 10.8% 5.3%
Cyberbullying perpetration
Never 659 731 12 0.018 650 781 27 <0.001 537 754 42 <0.001
87.3% 91.9% 79.5% 87.6% 81.7% 92.0%
Once or twice per month 67 42 117 93 72 51
8.9% 5.3% 14.3% 10.4% 11.0% 6.2%
2–3 times per month or more 29 22 51 18 48 15
3.8% 2.8% 6.2% 2.0% 7.3% 1.8%
Cyberbullying victimization
Never 682 709 0.3 0.99 703 727 13 0.014 562 706 16 0.003
86.5% 86.4% 81.5% 79.9% 81.8% 85.0%
Once or twice per month 59 62 82 118 51 80
7.5% 7.6% 9.5% 13.0% 7.4% 9.6%
2–3 times per month or more 47 50 78 65 74 45
6.0% 6.1% 9.0% 7.1% 10.8% 5.4%
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As shown in Table 3, at age 11, 34.0% of boys reported high maternal monitoring compared to 27.1% of girls (p = 0.004). By age 15, the figures increased to 53.5% for boys and 44.9% for girls (p < 0.001). More girls than boys at age 15 reported that their mothers knew about their friends (76.5% vs. 66.9%, p < 0.001). Moreover, 85.3% of boys and 90.8% of girls at age 11 stated their mothers knew where they went at night (p < 0.001), decreasing to 71.6% for boys and 81% for girls at age 15 (p < 0.001). When it comes to spending money, 62.8% of boys and 71.2% of girls aged 15 stated that their mother knows a lot about how they spend their money (p = 0.005). More girls compared to boys reported that the mother knows about where they are after school at the age of 11 (89.5% vs. 84.7%, p = 0.002) and at the age of 15 (79.7% vs. 73.6%, p = 0.022). In terms of internet use, statistical significance was found at the age of 11 (53.8% of boys and 58.6% of girls) and the age of 13 (33.6% of boys and 37.9% of girls, p = 0.019). For paternal monitoring, high monitoring was indicated by 50.5% of boys and 50.1% of girls at age 11, with a statistically significant difference found at age 13, where 54.0% of boys reported high monitoring compared to 60.4% of girls (p = 0.024).

Table 3.

Parental monitoring by age and gender.

Parental Monitoring Age 11 Age 13 Age 15
Boys Girls χ2 p Boys Girls χ2 p Boys Girls χ2 p
Maternal monitoring
High monitoring 249 214 11.3 0.004 378 342 16 <0.001 356 369 24 <0.001
34.0% 27.1% 45.4% 38.4% 53.5% 44.9%
Low monitoring 465 565 429 536 279 436
63.5% 71.4% 51.6% 60.2% 41.9% 53.1%
No monitoring 18 12 25 13 31 16
2.5% 1.5% 3.0% 1.5% 4.7% 1.9%
Mother knows a lot about…
Who student’s friends are 632 708 6.1 0.109 646 744 11 0.011 448 629 22 <0.001
84.3% 88.5% 76.3% 82.6% 66.9% 76.5%
How student spends his/her money 541 613 6.9 0.074 581 647 5.4 0.148 422 586 13 0.005
72.6% 76.7% 69.0% 72.1% 62.8% 71.2%
Where student is
after school		 629 714 14.9 0.002 676 746 4.9 0.178 493 656 9.6 0.022
84.7% 89.5% 80.3% 83.2% 73.6% 79.7%
Where student goes at night 631 724 18.7 <0.001 675 753 6.4 0.094 480 666 19 <0.001
85.3% 90.8% 80.1% 83.9% 71.6% 81.0%
What student does on the internet 401 468 9.4 0.024 283 341 9.9 0.019 186 249 6.9 0.077
53.8% 58.6% 33.6% 37.9% 27.6% 30.3%
Paternal monitoring
High monitoring 369 393 3.9 0.146 450 537 7.4 0.024 381 503 3.1 0.213
50.5% 50.1% 54.0% 60.4% 57.6% 61.3%
Low monitoring 327 369 340 308 235 256
44.7% 47.0% 40.8% 34.6% 35.5% 31.2%
No monitoring 35 23 43 44 46 62
4.8% 2.9% 5.2% 4.9% 6.9% 7.6%
Father knows a lot about…
Who student’s friends are 505 483 17.4 0.001 496 430 22 <0.001 364 373 19 <0.001
67.3% 60.1% 58.2% 47.7% 54.5% 45.3%
How student spends his/her money 459 481 0,8 0.838 481 471 11 0.010 364 393 13 0.005
61.5% 60.4% 56.9% 52.4% 54.4% 47.6%
Where student is
after school		 510 552 4.2 0.238 529 518 8.5 0.036 380 424 7.6 0.054
68.5% 69.1% 62.5% 57.9% 56.9% 51.5%
Where student goes at night 534 587 5.2 0.158 560 567 7.9 0.048 390 480 3.9 0.270
72.1% 74.1% 66.5% 63.3% 58.3% 58.2%
What student does on the internet 355 382 2.4 0.493 275 256 9 0.030 176 182 9.2 0.026
47.7% 48.0% 32.5% 28.6% 26.2% 22.1%
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More boys than girls at age 11 reported that their fathers knew about their friends (67.3% vs. 60.1%, p = 0.001), with 54.5% of boys and 45.3% of girls at age 15 indicating the same (p < 0.001). At age 15, 54.4% of boys and 47.6% of girls responded that their fathers know how they spend their money (p = 0.005). Also, more 13-year-old boys compared to girls stated that their father knows about where they are after school (62.5% vs. 57.9%, p = 0.036). Regarding paternal monitoring and internet use, statistical significance was found at the age of 13 (32.5% of boys and 28.6% of girls, p = 0.030) and age 15 (26.2% of boys and 22.1% of girls, p = 0.026).

Table 4 shows that boys aged 11 with no maternal monitoring had 23.21 times higher odds (CI 4.71–114.32) of high substance exposure compared to those with high maternal monitoring. Low maternal monitoring was also associated with increased odds for both high (4.23, CI 1.63–10.95) and low substance exposure (2.19, CI 1.46–3.27).

Table 4.

Multinomial logistic regressions by age and gender on the association between parental monitoring and exposure to multiple substances.

Age Substance Exposure Parental Monitoring Boys Girls
Sig. OR 95% Confidence Interval Sig. OR 95% Confidence Interval
Lower Bound Upper Bound Lower Bound Upper Bound
11 High vs. no
substance exposure		 No vs. high maternal
monitoring		 0 23.21 4.71 114.32 0.07 1.23 0.98 1.53
Low vs. high maternal
monitoring		 0 4.23 1.63 10.95 0.02 4.65 1.3 16.58
No vs. high paternal
monitoring		 0.99 1.01 0.2 5.05 0.12 1.08 0.98 1.2
Low vs. high paternal
monitoring		 0.49 0.72 0.28 1.84 0.25 2.4 0.54 10.65
Low vs. no
substance exposure		 No vs. high maternal
monitoring		 0.36 1.82 0.5 6.61 0.76 0.78 0.15 4.01
Low vs. high maternal
monitoring		 0 2.19 1.46 3.27 0 1.94 1.3 2.91
No vs. high paternal
monitoring		 0.36 1.48 0.64 3.43 0.04 2.64 1.07 6.56
Low vs. high paternal
monitoring		 0.88 1.03 0.69 1.54 0.01 1.65 1.11 2.44
13 High vs. no
substance exposure		 No vs. high maternal
monitoring		 0.08 3.52 0.85 14.55 0 14.04 2.61 75.55
Low vs. high maternal
monitoring		 0 3.23 1.81 5.78 0 3.87 2.4 6.25
No vs. high paternal
monitoring		 0.2 1.98 0.7 5.61 0.03 2.81 1.1 7.25
Low vs. high paternal
monitoring		 0.31 1.38 0.74 2.56 0.01 2.27 1.27 4.06
Low vs. no
substance exposure		 No vs. high maternal
monitoring		 0.03 3.11 1.09 8.83 0.11 3.9 0.73 20.82
Low vs. high maternal
monitoring		 0 1.82 1.25 2.64 0 2.08 1.46 2.96
No vs. high paternal
monitoring		 0.78 0.89 0.41 1.95 0.96 0.98 0.45 2.13
Low vs. high paternal
monitoring		 0.96 0.99 0.68 1.45 0.05 1.43 1.01 2.02
15 High vs. no
substance exposure		 No vs. high
maternal
monitoring		 0.02 4.63 1.34 16.01 0.27 2.25 0.54 9.44
Low vs. high
maternal
monitoring		 0 2.74 1.48 5.06 0 3.9 2.48 6.13
No vs. high
paternal
monitoring		 0.12 2.41 0.79 7.39 0 5.08 1.94 13.32
Low vs. high
paternal
monitoring		 0.31 0.72 0.39 1.35 0.01 1.87 1.16 2.99
Low vs. no
substance exposure		 No vs. high
maternal
monitoring		 0.73 0.79 0.21 3.01 0.43 1.74 0.44 6.91
Low vs. high
maternal
monitoring		 0.7 1.11 0.65 1.91 0 2.16 1.37 3.4
No vs. high
paternal
monitoring		 0.13 2.36 0.78 7.19 0.06 2.48 0.96 6.39
Low vs. high
paternal
monitoring		 0.22 1.41 0.81 2.45 0.73 0.93 0.61 1.42
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Among 13-year-old boys, low maternal monitoring was associated with 3.23 times higher odds of high substance exposure (CI 1.81–5.78), while no monitoring was linked to 3.11 times higher odds of low substance exposure (CI 1.09–8.83). Low maternal monitoring also increased the odds of low substance exposure (OR 1.82, CI 1.25–2.64).

For 15-year-old boys, no maternal monitoring resulted in 4.63 times higher odds of high substance exposure (CI 1.34–16.01), and low monitoring led to 2.74 times higher odds (CI 1.48–5.06).

In 11-year-old girls, low maternal monitoring increased the odds of high substance exposure by 4.65 times (CI 1.30–16.58) and, for low exposure, by 1.94 times (CI 1.30–2.91). No paternal monitoring was linked to 2.64 times higher odds for low substance exposure (CI 1.07–6.56), and low paternal monitoring was associated with a 1.65 times increase (CI 1.11–2.44).

For 13-year-old girls, high odds of substance exposure were linked to no maternal monitoring (OR 14.04, CI 2.61–75.55), low maternal (OR 3.87, CI 2.40–6.25), no paternal (OR 2.81, CI 1.10–7.25), and low paternal monitoring (OR 2.27, CI 1.27–4.06). Higher odds of low exposure were associated with low maternal (OR 2.08, CI 1.46–2.96) and low paternal monitoring (OR 1.43, CI 1.01–2.02).

For girls aged 15, higher odds of high substance exposure were linked to low maternal monitoring (OR 3.90, CI 2.48–6.13), no paternal monitoring (OR 5.08, CI 1.94–13.32), and low paternal monitoring (OR 1.87, CI 1.16–2.99). Low maternal monitoring was also associated with higher odds of low substance exposure (OR 2.16, CI 1.37–3.40).

As shown in Table 5, in 11-year-old boys, low maternal monitoring was associated with a 4.28 times increase (CI 2.11–8.67) in exposure to bullying, while no paternal monitoring increased odds by 6.85 times (CI 2.15–21.81). Low maternal monitoring was linked to a 2.35 times higher risk (CI 1.54–3.58) for low exposure to bullying.

Table 5.

Multinomial logistic regressions by age and gender on the association between parental monitoring and exposure to multiple instances of bullying.

Age Exposure to Bullying Parental
Monitoring		 Boys Girls
Sig. OR 95% Confidence
Interval		 Sig. OR 95% Confidence
Interval
Lower Bound Upper Bound Lower Bound Upper Bound
11 High vs. no exposure to
bullying 		No vs. high
maternal
monitoring		 0.1 3.23 0.76 13.71 0.5 2.27 0.21 23.99
Low vs. high
maternal
monitoring		 0 4.28 2.11 8.67 0.05 2.09 1.01 4.36
No vs. high
paternal
monitoring		 0 6.85 2.15 21.81 0.04 4.54 1.05 19.68
Low vs. high
paternal
monitoring		 0.9 1.02 0.5 2.1 0.02 2.54 1.15 5.61
Low vs. no exposure to
bullying		 No vs. high
maternal
monitoring		 0.3 0.39 0.07 2.08 0.61 1.51 0.32 7.14
Low vs. high
maternal
monitoring		 0 2.35 1.54 3.58 0 1.83 1.21 2.78
No vs. high
paternal
monitoring		 0.3 1.82 0.64 5.16 0.15 2 0.78 5.13
Low vs. high
paternal
monitoring		 0.3 1.24 0.83 1.84 0.01 1.66 1.14 2.44
13 High vs. no exposure to
bullying		 No vs. high
maternal
monitoring		 0 10.32 2.66 40.13 0 16.13 3.29 79.01
Low vs. high
maternal
monitoring		 0 2.34 1.21 4.5 0 3.13 1.65 5.92
No vs. high
paternal
monitoring		 0 3.29 1.06 10.25 0.01 5.8 1.54 21.88
Low vs. high
paternal
monitoring		 0.1 1.68 0.84 3.36 0.01 3.14 1.32 7.45
Low vs. no exposure to
bullying		 No vs. high
maternal
monitoring		 0.8 1.16 0.31 4.38 0.88 0.87 0.14 5.55
Low vs. high
maternal
monitoring		 1 1 0.68 1.47 0 1.91 1.35 2.71
No vs. high
paternal
monitoring		 0.1 1.91 0.82 4.44 0 4 1.85 8.64
Low vs. high
paternal
monitoring		 0 1.48 1.01 2.18 0 1.89 1.31 2.73
15 High vs. no exposure to
bullying		 No vs. high
maternal
monitoring		 0 9.06 2.78 29.47 0.91 1.01 0.84 1.21
Low vs. high
maternal
monitoring		 0 3.09 1.33 7.18 0.16 1.86 0.78 4.47
No vs. high
paternal
monitoring		 0.2 2.01 0.64 6.31 0.29 2.27 0.5 10.25
Low vs. high
paternal
monitoring		 0.4 0.7 0.3 1.63 0.43 1.54 0.53 4.5
Low vs. no exposure to
bullying		 No vs. high
maternal
monitoring		 0.5 0.67 0.19 2.37 0 5.19 1.7 15.84
Low vs. high
maternal
monitoring		 0.1 1.61 0.98 2.63 0 1.92 1.33 2.77
No vs. high
paternal
monitoring		 0.2 1.86 0.78 4.43 0 2.94 1.52 5.68
Low vs. high
paternal
monitoring		 0.9 0.98 0.59 1.64 0 2.11 1.34 3.33
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Among boys aged 13, no maternal monitoring was associated with 10.32 higher odds (CI 2.66–40.13), and low maternal monitoring with 2.34 higher odds (CI 1.21–4.50) for high exposure to bullying. Similarly, no paternal monitoring was linked to 3.29 higher odds (CI 1.06–10.25), and low paternal monitoring to 1.48 higher odds (CI 1.01–2.18) for low exposure to bullying.

For boys aged 15, no maternal monitoring was associated with 9.06 higher odds (CI 2.78–29.47), and low maternal monitoring with 3.09 higher odds (CI 1.33–7.18) for high exposure to bullying.

Among girls aged 11, low maternal monitoring was associated with 2.09 higher odds (CI 1.01–4.36), no paternal monitoring with 4.54 higher odds (CI 1.05–19.68), and low paternal monitoring with 2.54 higher odds (CI 1.15–5.61) for high exposure to bullying. Additionally, low maternal monitoring was associated with 1.83 higher odds (CI 1.21–2.78), and low paternal monitoring with 1.66 higher odds (CI 1.14–2.44) for low exposure to bullying.

For girls aged 13, no maternal monitoring was associated with 16.13 higher odds (CI 3.29–79.01), and low maternal monitoring with 3.13 higher odds (CI 1.65-5.29) for high exposure to bullying. No paternal monitoring increased the odds by 5.80 (CI 1.54–21.88), while low paternal monitoring increased the odds by 3.14 (CI 1.32–7.45). For low exposure to bullying, low maternal monitoring was associated with 1.91 higher odds (CI 1.35–2.71), no paternal monitoring with 4.00 higher odds (CI 1.85–8.64), and low paternal monitoring with 1.89 higher odds (CI 1.31–2.73).

Among girls aged 15, low exposure to bullying was associated with 5.19 higher odds (CI 1.70–15.84) for no maternal monitoring, 1.92 higher odds (CI 1.33–1.77) for low maternal monitoring, 2.94 higher odds (CI 1.52–5.68) for no paternal monitoring, and 2.11 higher odds (CI 1.34–3.33) for low paternal monitoring.

4. Discussion

This study examined distinct age patterns in exposure to multiple substances and bullying among boys and girls in Croatia. The proportions of multiple substance use increased with age for both boys and girls, while exposure to multiple instances of bullying is most common at the age of 13. Exposure to multiple instances of bullying is more common among boys than girls in all three age groups, while there is no clear gender pattern with exposure to multiple substances, which varies depending on age.

Even at the age of 11, a significant proportion of girls, and especially boys, are already exposed to multiple psychoactive substances and bullying. This indicates that prevention programs are already necessary in the lower grades of elementary school and that these programs should be comprehensive, rather than focused on a single substance or one form of violence.

The results of this study reveal significant associations between parental monitoring (both maternal and paternal) and multiple substance use as well as exposure to bullying among adolescents in Croatia. Patterns of substance use and bullying vary by gender and age, emphasizing the need for gender- and age-specific intervention strategies.

This study showed the strong protective effect of parental monitoring against both substance use and bullying, which aligns with previous research [18,20,22,23,24,25,26,28,29,31]. High levels of maternal monitoring were consistently associated with lower odds of both exposure to multiple substances and bullying involvement, confirming the critical role mothers play in adolescent behavioral development [37,38]. The findings are consistent with previous research showing that when mothers are more involved and emotionally connected with their children, they have a greater influence on reducing risky behaviors [32,33,34,36].

On the other hand, the level of paternal monitoring appeared to be more inconsistent. Although high paternal monitoring was generally associated with reduced exposure to substances and bullying, the protective effect was less consistent compared to maternal monitoring. This might be due to the traditional roles and expectations of mothers and fathers in Croatian society, where mothers may take on a more active role in adolescent everyday activities [39,40]. However, the strong association between the absence of paternal monitoring and higher substance use and bullying at the age of 13 indicates that fathers’ involvement becomes particularly crucial during early adolescence when adolescents are more susceptible to peer influence and external pressures [41].

5. Conclusions

This study shows the need for designing preventive strategies to reduce substance use and bullying among adolescents. Programs that strengthen parental monitoring, particularly maternal monitoring, should be prioritized. However, the results also indicate a need for greater paternal involvement, especially as children enter adolescence. Parenting interventions should encourage fathers to actively monitor their children’s social interactions and online activities to reduce risky behaviors.

It is demonstrated that boys and girls have different needs when it comes to preventing substance use and bullying, so interventions need to be designed specifically for each gender. For example, girls might need programs that address cigarette and e-cigarette use during their late adolescence, while boys might benefit more from earlier interventions aimed at reducing alcohol and energy drink consumption.

While this study provides valuable insights by focusing on the combined effects of multiple risk behaviors (substance use and bullying) and their associations with parental monitoring, it is not without limitations. This study relies on self-reported data, which may be subject to reporting bias and memory inaccuracies. The cross-sectional design of the research disables establishing causal relationships between variables. Future research could use longitudinal designs to track changes in parental monitoring and adolescent behavior over time.

Author Contributions

Conceptualization, M.V. and I.P.; Data Curation, M.V.; Formal Analysis, M.V. and L.S.; Investigation, M.V. and L.S.; Methodology, M.V., L.S. and F.S.; Project Administration, M.V. and L.S.; Resources, L.S. and F.S.; Software, M.V. and L.S.; Supervision, D.M. and I.P.; Validation, M.V., L.S., D.M. and F.S.; Visualization, M.V. and F.S.; Writing—Original Draft, M.V.; Writing—Review and Editing, M.V., L.S., D.M. and F.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Croatian Institute of Public Health on 10 March 2022. The ethical approval code is 381-15-22-2.

Informed Consent Statement

Participation was voluntary (with passive parental consent), and the answers were strictly confidential.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding Statement

This research received no external funding.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

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