Peer Review #2 of "Extent, patterns and demographic correlates for physical fighting among school-attending adolescents in Namibia: examination of the 2013 Global School-based Health Survey (v0.1)"
Bibliographic record
Abstract
Background: Physical fighting is particularly detrimental for young people, often affecting other areas of their developing lives, such as relationships with friends and family and participating in risky behaviors.We aim to quantify the amount of problematic physical fighting in Namibian adolescents and identify modifiable risk factors for intervention.Methods: We used the Namibia 2013 Global School-based Student Health Survey (GSHS).This survey collects health-related information on school-attending adolescents in grades 7 to 12.We defined physical fighting as having participated in at least two physical fights in the 12 months prior to responding to the survey.Factors that may be associated with physical fighting were identified a prior based on the literature and included age, sex, anxiety, suicide planning, truancy, physical activity, bullying victimization, presence of supportive parental figures, presence of helpful peers, extent of social network, and food insecurity.Multivariable logistic regression models were created to identify factors associated with physical fighting.Results: 4,510 adolescents were included in the study.52.7% female.16.9% of adolescents reported engaging in at least two physical fights in the previous year.Factors associated with an increased odds of physical fighting included having a suicide plan, anxiety, truancy, food deprivation and being bullied.Increased age and loneliness were associated with a decreased odds of physical fighting.Conclusion:This study identifies problematic physical fighting among adolescents in Namibia.We recommend public health and school-based programming that simultaneously targets risk behaviours and conflict resolution to reduce rates of physical fighting.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.008 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.409 | 0.214 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".