From focus groups to production of a distracted driving video
Bibliographic record
Abstract
BACKGROUND: The Impact program is an adolescent, injury prevention program with both school- and hospital-based components aimed at decreasing high-risk behaviors and preventing injury. The objective of this study was to obtain student input on the school-based component of Impact, as part of the program evaluation and redesign process, to ensure that the program content and format were optimal and relevant, addressing injury-related issues important for youth in our region. METHODS: Secondary schools were selected in various geographic regions with students varying in language, religion, and socioeconomic status. A mixed-methods questionnaire was developed and pretested on program content, format, relevance, quality, and effectiveness. Attitude and opinion questions on issues facing teens today were ranked on a 7-point Likert scale. Open-ended, qualitative questions were included in the focus groups, with responses themed. RESULTS: There were 167 respondents in the nine geographically, socioeconomically, and culturally diverse focus groups with a mean age of 16 years, 52% were male, and 69% were in Grade 11. Ninety-three percent of respondents rated the content of Impact as comprehensive (median, 6 of 7, with 7 being very comprehensive), and 29% rated the format a 5 of 7. Impact was rated relevant (89%), addressing issues for teens (median, 6 of 7). Issues suggested to highlight included texting and driving, drugs, partying, self-harm, and abusive relationships. Texting while driving was perceived as a significantly more common (81%) injury issue for adolescents compared with other driving risk factors (p < 0.001), with one student commenting, "If you don't (text and drive), you either don't have a phone or don't have a driver's license." CONCLUSION: Injury prevention programs must be continually evaluated to ensure they are relevant, addressing issues important for youth, and presented in a format that resonates with the audience. Student focus groups identified motor vehicle collisions and texting as important issues as well as a desire for teens to hear personal stories with a visual element. This provided the information needed to develop the next logical direction for our program, the production of a distracted driving video ("Distracted Driving: Josh's Story," http://youtu.be/BFPke9gBybc) to be incorporated into school presentations. LEVEL OF EVIDENCE: Epidemiologic/prognostic study, level III.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".