| Literature DB >> 34041356 |
Aliffitri Ali Zohor Ali1, Noor Azimah Muhammad2, Teh Rohaila Jamil2, Saharuddin Ahmad2, Noor Azah Abd Aziz2.
Abstract
Despite the wide accessibility to internet, pornography activities among youths are not well described in conservative countries like Malaysia. This study aimed to determine the prevalence, elicit gender differences and identify associated factors of pornographic exposure including perceived realism among college students. This cross-sectional study was conducted among students aged 18 to 25 years from seven colleges in Penang, Malaysia. A self-administered questionnaire was used to obtain data on socio-demographic characteristics, pornography exposure, pattern of use, sexual exposure and perceived realism. Among 986 participants, the prevalence of lifetime pornography exposure was 74.5%. More males (71.7%) were exposed to pornography, had started at earlier age, were frequent users and using the internet alone at home (p < 0.001). Males had higher odds of having exposure (Adjusted odds ratio, AOR = 20.44, 95% CI: 12.50 - 33.42, p < 0.001), whilst those who perceived pornography as real had lower odds of having pornographic exposure (AOR = 0.64, 95% CI: 0.43-0.94, p = 0.02). There is a high prevalence of pornography exposure among Malaysian college students, especially involving males. Easy access to pornographic materials at home facilitates this activity. Gender and perceived realism determined their pornographic exposure. Thus, parental monitoring of online access and planning structured activities to occupy free time are recommended at an early age.Entities:
Keywords: Adolescents; Gender; Perceived realism; Pornography; Youth
Year: 2021 PMID: 34041356 PMCID: PMC8144352 DOI: 10.1016/j.abrep.2021.100350
Source DB: PubMed Journal: Addict Behav Rep ISSN: 2352-8532
Socio-demographic characteristics of participants (n = 986).
| Variables | n (%) |
| Age [mean (SD)] years | 19.13 (±1.56) |
| Gender | |
| Male | 557 (56.5) |
| Female | 429 (43.5) |
| Ethnicity | |
| Malay | 895 (90.8) |
| Chinese | 11 (1.1) |
| Indian | 73 (7.4) |
| Others | 7 (0.7) |
| Marital status | |
| Not married | 974 (98.8) |
| Married | 12 (1.2) |
| Program | |
| Certificate | 306 (31.0) |
| Diploma | 616 (62.5) |
| Degree | 64 (6.5) |
| Free internet availability (hostel/house) | |
| Yes | 515 (52.2) |
| No | 471 (47.8) |
| Family monthly household income, RM [median (IQR)] | 2000.00 (±3400.00) |
Prevalence and pattern of pornographic viewing stratified by gender (n = 986).
| Variables | All N (%) | Male n (%) | Female n (%) | P-value |
| Lifetime exposure to pornography | ||||
| Yes | 735 (74.5) | 527 (71.7) | 208 (28.3) | <0.001 |
| No | 251 (25.5) | 30 (12.0) | 221 (88.0) | |
| Ever watched pornography | ||||
| Yes | 605 (61.4) | 496 (82.0) | 109 (18.0) | <0.001 |
| No | 381 (38.6) | 61 (16.0) | 320 (84.0) | |
| Ever read pornography | ||||
| Yes | 416 (42.2) | 328 (78.9) | 88 (21.1) | <0.001 |
| No | 570 (57.8) | 229 (40.2) | 341 (59.8) | |
| Age expose to pornography (n = 675) | ||||
| mean (SD) years | – | 14.1 (±2.1) | 15.8 (±2.5) | <0.001 |
| Frequency of usage (n = 735) | ||||
| Never & < one month | 400 (54.4) | 227 (56.8) | 173 (43.2) | <0.001 |
| ≥ one month | 335 (45.6) | 300 (89.5) | 35 (10.4) | |
| Sources of pornography (n = 561) | ||||
| Internet | 496 (88.4) | 405 (81.7) | 91 (18.3) | <0.001 |
| Others | 65 (11.6) | 36 (55.4) | 29 (44.6) | |
| Location of usage (n = 621) | ||||
| At home | 523 (84.2) | 428 (81.8) | 95 (18.2) | <0.001 |
| Others | 98 (15.8) | 57 (58.2) | 41 (41.8) | |
| Interpersonal context of usage (n = 630) | ||||
| Alone | 533 (84.6) | 428 (80.3) | 105 (19.7) | 0.004 |
| Others | 97(15.4) | 65 (67.0) | 32 (33.0) | |
| Frequency of paid pornography (n = 735) | ||||
| Never | 645 (87.8) | 448 (69.5) | 197 (30.5) | <0.001 |
| Once and more | 90 (12.2) | 79 (87.8) | 11 (12.2) | |
| Money spent per month (RM) (n = 79) | ||||
| Minimum | 0.5 | 1 | ||
| Maximum | 400 | 1000 | ||
| Median (IQR) | 10.00(±25.00) | 5.00(±7.00) | <0.001 | |
Bivariate and multivariate analysis of factors associated with lifetime pornography exposure (n = 986).
| Variables | Simple logistic regression | Multiple logistic regression analysis | ||||||
|---|---|---|---|---|---|---|---|---|
| Wald | Crude Odds Ratio | 95% confidence intervals | P-value | Wald | Adjusted Odds Ratio | 95% confidence intervals | P-value | |
| Age (mean) years | 0.49 | 0.97 | 0.89–1.06 | 0.484 | 0.58 | 1.05 | 0.93–1.18 | 0.448 |
| Gender: Male [Female] | 192.20 | 18.66 | 12.34–28.23 | <0.001 | 144.69 | 20.43 | 12.50–33.42 | <0.001 |
| Ethnicity: Malay [Non-Malay] | 1.48 | 1.34 | 0.84–2.14 | 0.223 | 1.80 | 1.57 | 0.81–3.02 | 0.180 |
| Internet availability: Yes [No] | 11.13 | 0.61 | 0.45–0.81 | 0.001 | 0.01 | 0.99 | 0.66–1.50 | 0.970 |
| Family monthly income: Low [High] | 3.16 | 0.75 | 0.55–1.03 | 0.075 | 1.07 | 0.80 | 0.53–1.21 | 0.300 |
| Perceived realism: Agree [Disagree] | 9.85 | 0.60 | 0.43–0.82 | 0.002 | 5.06 | 0.64 | 0.43–0.94 | 0.020 |
| Sexually active: Yes [No] | 4.75 | 3.73 | 1.14–12.20 | 0.029 | 3.35 | 3.36 | 0.92–12.42 | 0.070 |
| Education program: Diploma and higher [Certificate] | 0.02 | 1.02 | 0.75–1.40 | 0.890 | – | – | – | – |
| Sexual orientation: Non-heterosexual [Heterosexual] | 0.31 | 0.55 | 0.07–4.52 | 0.580 | – | – | – | – |