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Record W2031392157 · doi:10.1177/1049731509351988

Interventions to Prevent and Reduce Cyber Abuse of Youth: A Systematic Review

2010· review· en· W2031392157 on OpenAlexaff
Faye Mishna, Charlene Cook, Michael Saini, Meng‐Jia Wu, Robert J. MacFadden

Bibliographic record

VenueResearch on Social Work Practice · 2010
Typereview
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe InternetPsychological interventionPsychologyApplied psychologyInternet privacyMedicinePsychiatryComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Objectives: The Internet has created a new communication tool, particularly for young people whose use of electronic communication is exploding worldwide. While there are many benefits that result from electronic-based communication, the Internet is concurrently a potential site for abuse and victimization. Methods: This paper systematically reviews the effectiveness of cyber abuse interventions in increasing Internet safety knowledge and decreasing risky online behavior. Results: Significant results were found between pre- and posttest scores related to Internet safety knowledge. Most results related to risky online behavior were not significant. 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 behavior.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.278
GPT teacher head0.555
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations151
Published2010
Admission routes1
Has abstractyes

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