Interventions for Children, Youth, and Parents to Prevent and Reduce Cyber Abuse
Bibliographic record
Abstract
This Campbell Systematic Review examines the effectiveness of cyber abuse interventions in increasing internet safety knowledge and decreasing risky online behaviour. The review summarises findings from three studies: one conducted in Canada and the other two in the USA. The participants were middle school students in grades five to eight between the ages of 5-19 who use the internet or cell phones. A total of 2,713 participants were included in the studies. Cyber abuse interventions and preventions are associated with an increase in internet safety knowledge. Despite the increase in knowledge, students who received the intervention did not become less likely to engage in inappropriate online behaviour, such as disclosing one's name, participating in open chat rooms, or emailing strangers. The three studies were evaluations of the following cyber abuse interventions: I-SAFE cyber safety program, the missing cyber safety program, and the in-school cyber bullying intervention (HAHASO). The I-SAFE cyber safety had the largest effect on internet safety knowledge. Both the missing program and HAHASO suggests that intervention did not significantly change internet-related safety attitudes or reduce the number of reported cyber bullying experiences. Given the low number of studies available for rigorous cyber abuse prevention and intervention evaluations, the evidence base for these conclusions is weak. Executive summary/Abstract BACKGROUND The Internet has created a new communication tool, particularly for young people whose use of e-mail, websites, instant messaging, web cams, chat rooms, social networking sites and text messaging is exploding worldwide. While there are many benefits that result from electronic based communication, the Internet is, however, concurrently a potential site for abuse and victimization, whereby young people can fall victim to sexual perpetrators, stalkers, exploiters, and peers who bully online. Interventions regarding cyber abuse have been developed in response to a growing emphasis on protecting children and youth from online dangers. OBJECTIVES To examine the effectiveness of cyber abuse interventions in increasing Internet safety knowledge and decreasing risky online behaviour. SELECTION CRITERIA The scope of this review is experimental and quasi-experimental prevention and intervention strategies that target children ages 5 to 19 years old and/or their parents, utilize a control group, and examine an outcome related to cyber abuse such as Internet safety knowledge, risky online behaviour, or exposure to inappropriate online content. SEARCH STRATEGY We searched the following databases : Psychological Abstracts (PsycINFO, PsycLIT, ClinPsyc-clinical subset); MEDLINE; EMBASE; Database of reviews of effectiveness (DARE online); ChildData (child health and welfare); ASSIA (applied social sciences); Caredata (social work); Social Work Abstracts; Child Abuse, Child Welfare & Adoption; Cochrane Collaboration; C2-SPECTR; Social Sciences Abstracts; Social Service Abstracts; Dissertation Abstracts International (DAI). We also handsearched Youth and Society; Journal of Interpersonal Violence; Annual Review of Sex Research; Computers in Human Behavior; Computers & Education; and Journal of Adolescent Health. Additionally, we contacted experts in the field and searched for grey literature. DATA COLLECTION AND ANALYSIS Two screeners reviewed abstracts and full-text of all articles. Three articles met all inclusion criteria, and effect sizes and z-tests were calculated for all relevant outcomes. MAIN RESULTS Significant z-tests were found between pre-and post-test scores on measures related to Internet safety knowledge such as managing online risk and identifying online predators. Most z-tests related to pre- and post- measures of risky online behaviour were not significant, including disclosing one's name, participating in open chat rooms, or emailing strangers. REVIEWERS’ CONCLUSIONS Results provide evidence that participation in psychoeducational Internet safety interventions is associated with an increase in Internet safety knowledge but is not significantly associated with a change in risky online behaviour. The need for further research in this field is highlighted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".