Prescription opioid medications : behaviour, knowledge, and beliefs of potential risks among youth undergoing oral surgery procedures
Bibliographic record
Abstract
Canadian youth have demonstrated the fastest-growing rates of opioid-related hospitalizations in the past decade. Prescription opioid medications, although effective in alleviating severe post-operative pain, also have the potential for misuse and diversion. Oral surgery procedures are often the first exposure to opioids for youth patients. This population has been understudied despite being substantially affected by the opioid crisis. The aim of this study was to determine how informed youth (ages 15 to 24) are about potential risks associated with prescription opioid use following oral surgical procedures. An anonymous, 32-item survey was administered through Qualtrics to youth patients following oral surgical procedures to assess behaviour, knowledge, and beliefs about prescription opioid use. Participants were recruited from ten oral surgery practices in British Columbia during six months. A total of 177 responses were recorded. 121 (68.4%) participants received an opioid prescription, and only 2 (2%) participants stored their medication in a locked location. For those who did not, 80.7% indicated they were not worried about anyone taking it. Of the 84 (69.4%) participants with leftover pills (mean: 17.7 pills/participant), 71 (84.5%) reported still having them in their possession, with 33 (46.5%) intentionally saving them for future need. Compared to females, males were more likely to believe that prescription opioids are safer than other substances (P=0.006), and that they are less likely to get into trouble using them (P=0.046). Multivariate logistic regression models demonstrated that the male sex is a significant predictor of prescription opioid misuse (P=0.043). This study indicates a general lack of knowledge among participants and demonstrates that youth are not well informed of the potential risks associated with prescription opioids. In conclusion, it is essential to implement a practical approach to inform youth of the implications surrounding prescription opioid use and its potential for adverse health outcomes.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".