Digital Disconnection: A Qualitative Study of Youth and Young Adult Perspectives on Cyberbullying and the Adoption of Auto-Detection or Software Tools
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to understand the needs of youth and young adults, current gaps around safeguarding social media, and factors affecting adoption of data-driven auto-detection or software tools. METHODS: This qualitative study is the first step of a larger initiative that aims to use participatory action research and co-design principles to develop a digital tool that targets cyberbullying. Youth and young adults aged 16-21 years were recruited to participate in semistructured focus groups between March 2020 and November 2021. Thematic analysis was used to develop themes, with a member-checking process to validate the findings. RESULTS: Six focus groups were completed with 39 participants and five themes were generated from the analysis. Participants described the mental health impacts of cyberbullying on young people, the stigma associated with it, and the need for more mental health resources. They felt that additional efforts are needed to improve the school environment, school-based interventions, and training protocols to ensure that youth feel safe reporting cyberbullying. Most participants were open to using a digital solution but raised concerns around the trustworthiness of artificial intelligence and wanted it to be co-designed with young people, integrated across platforms, informed by data-driven decisions, and transparent with users. DISCUSSION: Youth and young adults are accepting of a low-risk digital cyberbullying solution as current interventions are not meeting their needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".