The relationship between strengths in youth and bullying experiences at school
Bibliographic record
Abstract
Bullying is a serious problem plaguing educational systems and influencing the lives of many youth. The consequences of bullying, for both bullies and victims, are numerous and well-established, including increased emotional and behavioural problems, difficulties in peer relationships, and poor academic achievement. To date, little research has been completed that explores the role of strengths in the bullying experiences of youth. An understanding of students’ strengths could aid in the development of appropriate intervention and prevention programming and to promoting the well-being of youth. Therefore, this study examined the relationships between strengths in youth and bullying experiences within a comprehensive strength-based framework. Participants were 263 students (112 males) recruited from Grades 7 and 8 classes in Ontario, Canada. Participants completed self-report questionnaires on bullying and victimisation experiences and a broad range of personal strengths. The results of this study did not support a direct relationship between overall strengths and bullying behaviours and victimisation experiences. However, some specific strengths were identified as predictors of both bullying and victimisation. Of note, while having more strengths in some domains predicted reduced rates of bullying and victimisation, having more strengths in other domains predicted increased rates of both bullying and victimisation, suggesting a masking effect. These results highlight the importance of further exploring the relationship between strengths and bullying as well as the possible benefits of providing strength-based intervention and prevention programmes.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".