Investigating the Association Between Active Transportation to School and Bullying in Canadian Schoolchildren
Bibliographic record
Abstract
Bullying, or the act of using direct or indirect physical and verbal tactics to distress or control another, has been recognized as an important problem among child populations internationally. The wide prevalence and deep impact of bullying indicates the need for targeted program implementation to eliminate the bullying epidemic. Focusing on new associations between bullying and other health behaviours will give insight and direction for new, more targeted bullying eradication approaches. The association explored in this undergraduate thesis project is between bullying and active transportation, defined in this study as walking and bicycling. Engagement by students in active forms of transportation to school is generally on the decline in North America. This is of concern, as methods of transportation including cycling and walking are forms of physical activity that can be an important indicator of improved health. Planned analyses will be based on the sixth (2009/10) Canadian cycle of the Health Behaviour in School-Aged Children (HBSC) Study. The HBSC study is a World Health Organization cross-national study focused on the investigation of health behaviours among adolescents in 43 participating countries. A variety of statistical analysis tools will be employed to determine whether a link can be drawn between students who engage in active transportation and their involvement with bullying (whether as a victim, a perpetrator, or both). This will provide foundational information for health promotion efforts. By targeting the concerns of youth and their parents and providing objective evidence about the bullying risks (real or perceived) involved in active transportation, student may be encouraged to adopt a healthier, more active lifestyle that could eventually translate into lower obesity rates and a healthier population.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".