From Words to Wounds: Cyberbullying and Its Influence on Mental Health Across the Lifespan
Bibliographic record
Abstract
Cyberbullying can be prevalent across different life stages, with lasting traces on mental health across the lifespan. This study aims to (a) explore how cyberbullying is emotionally experienced across three distinct age groups and (b) analyze the influence of cyberbullying on mental health across the lifespan. This study included 883 participants divided into three age groups: 18-39, 40-59, and 60+. In-depth semi-structured interviews were conducted to gather participants' experiences and perspectives. The data were then subjected to content analysis, which revealed a number of themes. The first objective revealed the following themes: For ages 18-39: (a) feeling ashamed or humiliated (92.4%), (b) withdrawing from friends and family, and (c) experiencing harassment as positive and difficulties with rules. For ages 40-59: (a) losing interest in hobbies (89.5%), (b) questioning about things they did or did not do, and (c) experiencing a sense of missing out. For ages 60+: (a) negative thoughts and self-talk (91.3%), (b) feeling judged negatively, and (c) feeling financially vulnerable. The second objective showed: For 18-39: (a) depressive symptoms (79.7%), (b) easy anger, and (c) suicidal behavior. For 40-59: (a) anxiety (93.2%), (b) low self-esteem, and (c) the use of substances. For 60+: (a) frustration (78.1%), (b) isolation, and (c) disturbances in sleep and eating patterns. This study highlights the significant psychological and emotional impact of cyberbullying across age groups, emphasizing the need for targeted interventions that address the unique challenges faced by individuals at different life stages. The findings underscore the importance of developing age-specific strategies to mitigate the effects of cyberbullying and to have perpetrators take responsibility for their reckless disregard for others, and ultimately, themselves.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".