Bullied Because of Their Teeth: Evidence from a Longitudinal Study on the Impact of Oral Health on Bullying Victimization Among Australian Indigenous Children
Bibliographic record
Abstract
Making life better for Indigenous peoples is a global priority. Although bullying and oral health have always been a topic of concern, there is limited information regarding the impact of this problem in the general population, with no evidence in this regard among the Australian Indigenous population. Thus, we aimed to quantify the relationship between bullying victimization and oral health problems by remoteness among 766 Australian Indigenous children aged between 10–15-year-olds using data from the LSIC study. Bivariate and multilevel mixed-effect logistic regression analyses were employed. Findings indicated children self-reported bullying more than parents reported their children were being bullied (44% vs 33.6%), with a higher percentage from rural/remote areas than urban areas. Parents reported oral health problem increases the probability (OR 2.20, p<0.05) of being bullied in Indigenous children living in urban areas. Racial discrimination, lower level of parental education and poor child oral hygiene increase the risk of bullying victimization. Parental happiness with life and a safe community was associated with a lower risk of bullying. Dental problems are linked with Australian Indigenous children experiencing bullying victimization. Cultural resilience and eliminating discrimination maybe two modifiable paths to ameliorating health issues associated with bullying in the Australian Indigenous community.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".