Interventions for Children, Youth, and Parents to Prevent and Reduce Cyber Abuse
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».