Effectiveness of the P.A.R.T.Y. (Prevent Alcohol and Risk-Related Trauma in Youth) Program in Preventing Traumatic Injuries: A 10-Year Analysis
Bibliographic record
Abstract
BACKGROUND: The P.A.R.T.Y. (Prevent Alcohol and Risk-Related Trauma in Youth) program is a 1-day injury awareness and prevention program for youth aged 15 years and older. The goal is to teach adolescents to recognize their injury risks and make informed decisions to reduce them. This study assessed the effectiveness of the P.A.R.T.Y. Program in preventing traumatic injuries during a period of 10 years (1992-2004). METHODS: P.A.R.T.Y. participants (STUDY) were matched with subjects having the same age, gender, residential area, and initial year in database, who did not attend the P.A.R.T.Y. Program (CONTROL). Data from hospital discharge database, and provincial health claims, were searched to determine the incidence of traumatic injuries in both groups. Statistical comparisons were made for the two groups, gender, calendar year, and before and after the graduating driver licensing system was implemented, using the χ and conditional logistic regression analysis with a p<0.05 considered significant. RESULTS: Of 3,905 P.A.R.T.Y. participants, 1,281 were successfully randomly matched on the above 4 variables with 1,281 controls. The most frequent injury was injury by other or homicide 373 of 2,562 (14.8%). There were fewer traumatic injuries in the STUDY group than in the CONTROL group (43.3% vs. 47.4%; p=0.02; OR, 1.22; 95% CI, 1.03-1.45). This difference was stronger in females (44.4% vs. 49.0%; p=0.04) and before the graduating driver licensing system implementation (60.1% vs. 67.2%; p=0.04). CONCLUSIONS: The P.A.R.T.Y. Program effectively reduced the incidence of traumatic injuries among its participants. This effectiveness was stronger among females and before the driver licensing system was implemented.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".