Teen Driver Distractions and Parental Norms
Bibliographic record
Abstract
Research related to parental norms on teen driver distractions is limited, although distracted driving is a serious concern for teens. This paper investigates whether teens' perception of their parent's engagement in and approval of distractions is different to what their parent reports, and whether any discrepancy relates to teens' self-reported distraction engagement frequency. It also investigates whether there are discrepancies between the parents' perception of their teen's distraction engagement frequency and the teen's self-report. A distinction is made between legal and illegal distractions as drivers may build stronger norms around illegal distractions. Analyses were conducted on data from 63 teen-parent dyads from Ontario, Canada, who completed an online survey, including self-reported engagement in 16 distractions and related descriptive (what parents/teens do) and injunctive (what parents approve/disapprove) norms. Dyads were divided into two groups: higher-engagers (n = 27) and lower-engagers (n = 36) based on teens' self-reported engagement frequency. Higher-engagers reported engaging in both distraction types (legal and illegal) more often than their parent did; there was no difference between lower-engagers and their parent. Higher-engagers' perception of their parent's engagement in and approval of legal distractions was higher than their parent's self-report, while these parents perceived their teen's engagement in both distraction types to be lower compared with the teen's self-report. The only discrepancy observed for lower-engagers was that teens' perception of their parent's approval of legal distractions was higher than parents' self-reports. Our findings suggest that misperceptions may exist for teens who engage more frequently in distractions and for their parents, who may benefit from relevant interventions.
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 | 0.003 | 0.019 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".