| Literature DB >> 33198703 |
Xueyan Yang1, Moye Xin2, Kun Liu1, Bilun Naz Böke3.
Abstract
BACKGROUND: We attempted to find if there were gender differences in Non-suicidal self injurious (NSSI) behaviors and Suicidal ideation among Chinese adolescents, then analyze the impact of Internet use frequency on these variables among adolescents of different genders.Entities:
Keywords: Adolescents; Gender difference; Internet use frequency; NSSI; Suicidal ideation
Mesh:
Year: 2020 PMID: 33198703 PMCID: PMC7670714 DOI: 10.1186/s12889-020-09866-0
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Gender differences of Internet use frequency among adolescents
| Internet Use Frequency | Male(803) | Female(1215) | ||
|---|---|---|---|---|
| M | M | |||
| IM softwares | 4.39 | 3.64 | 4.49 | 3.13 |
| Social softwares | 4.13 | 2.74 | 4.42 | 2.80 |
| Video softwares | 4.07 | 3.12 | 4.14 | 3.28 |
| Shopping softwares | 3.04 | 0.74 | 3.67 | 0.90 |
| Knowledge sharing softwares | 4.12 | 1.87 | 2.61 | 0.86 |
| Online gaming | 3.33 | 0.85 | 2.09 | 1.07 |
Note. + , p < 0.1;*, p < 0.05;**, p < 0.01;***, p < 0.001
The Impact of Internet Use Frequency on the Prevalence of NSSI Behavior Among adolescents by Gender
| Dependent: whether NSSI behavior occurs (reference: No) | Males | Females | |||
|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | ||
| Independent Variable: Internet use frequency | IM softwares | 0.054 | 0.985 | 0.912 | 1.024 |
| Social softwares | 0.786* | 1.012** | 0.993*** | 1.025*** | |
| Video softwares | 1.034 | 1.055 | 0.972 | 0.860 | |
| Shopping softwares | 0.985 | −0.900 | 1.139 | 1.016 | |
| Knowledge sharing softwares | −1.091** | − 1.097*** | 0.994 | 0.984 | |
| Online gaming | 0.927 | 1.840 | 1.307 | 0.950 | |
| Control variables | Age | 1.030 | −0.886 | ||
| Only-child (reference: No) | −0.746** | −1.117* | |||
| Father’s educational level (reference:primary school and below) middle school or above | −0.793 | −1.125 | |||
| Mother’s educational level (reference:primary school and below) middle school or above | −1.279 | −1.089 | |||
| Parents’ marital status (reference:separated) Married | −0.762*** | −0.969** | |||
| 95% CI | 0.73, 0.86** | 0.84, 1.05** | 1.22, 0.94** | 1.30,1.10** | |
| −2 Log Likelihood | 607.07** | 591.36*** | 787.32*** | 845.21*** | |
| Cox & Snell R2 | 0.003 | 0.023 | 0.002 | 0.025 | |
| Nagelkerke R2 | 0.005 | 0.037 | 0.004 | 0.040 | |
Note. + p < 0.1;*p < 0.05;**p < 0.01;***p < 0.001
The Impact of Internet Use Frequency on the Intensity of Suicidal Ideation Among adolescents by Gender
| Dependent: Suicidal Ideation Intensity | Males | Females | |||
|---|---|---|---|---|---|
| Model 5 | Model 6 | Model 7 | Model 8 | ||
| Independent Variable: Internet use frequency | IM softwares | −0.175 | −0.188 | 0.012 | 0.052 |
| Social softwares | 0.037*** | 0.046*** | 0.163* | 0.159** | |
| Video softwares | −0.008 | −0.041 | 0.038 | 0.033 | |
| Shopping softwares | 0.077 | 0.004 | 0.128 | 0.082 | |
| Knowledge sharing softwares | −0.031 | −0.045 | −0.036 | − 0.021 | |
| Online gaming | 0.124* | 0.119* | 0.071 | 0.087 | |
| Control variables | Age | −0.212*** | −0.135* | ||
| Only-child (reference: No) | 0.037 | 0.003 | |||
| Father’s educational level (reference:primary school and below) middle school or above | 0.073 | −0.016 | |||
| Mother’s educational level (reference:primary school and below) middle school or above | 0.109 | −0.046 | |||
| Parents’ marital status (reference:separated) Married | −0.163*** | −0.046** | |||
| 95% CI | 0.61, 0.91*** | 0.75, 1.03*** | 1.03, 0.74** | 1.26,0.93** | |
| F | 3.255*** | 5.133*** | 2.132** | 2.655*** | |
| df | 723 | 723 | 1187 | 1187 | |
| Adjusted R2 | 0.14 | 0.45 | 0.08 | 0.33 | |
Note. + p < 0.1;*p < 0.05;**p < 0.01;***p < 0.001