Canadian Adolescent and Young Athletes Knowledge of Pain-relieving medications
Bibliographic record
Abstract
Purpose: Adolescent knowledge of pain-relieving medications (PRM) and opioids is scant, particularly from a Canadian perspective. Research indicates that athletes will often resort to PRM and opioids to manage their pain, but it is unknown whether athletes are fully informed as to its use and misuse including adverse events that may occur. The purpose of this study is to understand what elite level male athletes know about use, contraindications, and recommendations of pain-relieving medications; particularly opioids to manage their pain. Methods: Using a qualitative, grounded theory approach, we interviewed male athletes to understand their level of knowledge regarding pain-relieving medications, particularly opioids. Athletes were approached through their coaches/teams and after receiving consent (assent from those under the age of 18) were informed of their rights as a participate. Ethical approval was received from Sheridan College’s REB. Results: Eight male athletes (aged 15-23 years, SD=3.01) who were involved in elite level basketball and lacrosse were interviewed. From the data, 4 themes emerged: 1) Athletes lacked formal education on pain-relieving medication including opioids ; 2) Athletes were influenced by coaches, peers, culture and health care providers on whether to use pain-relieving medications; 3) Athletes trusted guardians, Athletic Therapists affiliated with teams, and coaches to provide advice regarding use of pain-relieving medications; 4) Athletes understood that pain was a “part of the game”. Conclusions: Athletes lacked understanding of the use of pain-relieving medications including opioids and indicated that under certain circumstances, their use would be appropriate. As a result, athletes may engage in misuse of pain-relieving medications without fully understanding their appropriate use or consequences. Their sources of information on PRM and opioids range from coaches, peers, internet, culture, parents/guardians, and Athletic Therapists. The information found in this study is important for health care providers to provide opportunities to educate athletes on the use of pain-relieving medications. Athlete’s misuse of pain-relieving medication without the health care providers knowledge can have implications such as faulty assessment, diagnosis, and management of injuries, potentially creating opportunities for further harm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".