Parental influence on adolescent risk behaviours: a strategy to empower parents
Bibliographic record
Abstract
Background Adolescence is a developmental period marked by a rise in risk-taking, injury and mortality rates. Mortality rates increase by 200% during this developmental period. This rise has been related to adolescents' increased involvement in maladaptive risk-taking (eg, risky driving). Research highlights the importance of teenagers' parents in the risk decisions they make. Aims/Objectives/Purpose In order to deepen the understanding of parental influence on risk-taking, Parachute, sponsored by State Farm Insurance, conducted an in-depth study and has begun developing parent programming. Methods An extensive literature review revealed that parents have more influence than they may be aware of. A Canada-wide survey was conducted on 300 parents (76 males, 224 females) and their 300 teenagers (150 males, 148 females) to test the theories emerging from the literature review. Parent and child responses to these surveys were compared using paired-samples t tests and regression analyses. Results/Outcome Analyses demonstrated significant relationships between parental variables and levels of parental influence. In fact, regression analyses revealed that a child's perception of their parent's risk-taking behaviours significantly predicted 30% of the variability in their risk-taking frequencies. Significance/Contribution to the Field Overall, it was found that Canadian parents have a great deal of influence over their children's risk-taking, through their behaviours and home environments. These results support the need to work with parents around their influence over their teens' behaviours. Parachute has begun developing parent programming to engage and empower parents around becoming the most positive influence possible, aiding in the long-term reduction of injuries and deaths.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".