Sport Health Care Providers Knowledge of Pain-Relieving Medications Including Opioids
Bibliographic record
Abstract
Background: Sport health care providers (SHCP) frequently field inquiries from athletes and key stakeholders regarding pain-relieving procedures, including medications. Purpose: This study sought to investigate the knowledge and role of SHCPs in managing and dispensing pain-relieving medications (PRMs), including opioids, for Canadian athletes. Method: Certified athletic therapists, registered sport massage therapists, licensed doctor of chiropractic sport sciences, and registered physiotherapists were sent an online survey. Percentage responses for each question as well Chi-squared analyses to determine potential interactions between questions were calculated. Results: Findings revealed a significant majority of participants (80%) self-reported having a “great deal” or “some” knowledge about PRMs with approximately half (49.4%) feeling confident to address their athletes’ needs. Many participants indicated they lacked adequate information related to opioids. Participants selected “physician” or “pharmacist” when asked about their preferred sources of information on opioids, revealing a reliance on medical professionals for guidance regarding opioid use in sports health care. More than half of the participants did not possess formal professional education on PRMs or opioids, and those who did have formal education expressed dissatisfaction with the adequacy of their education concerning PRMs and opioids. Furthermore, SHCPs who participated in the study expressed a lack of confidence in their knowledge of opioids, hindering their ability to provide comprehensive answers to athletes’ questions about pain management. They stressed that having more knowledge would boost their confidence in addressing athletes inquires about pain relief. Conclusions: This study underscores the urgent need for improved formalized education for SHCPs regarding PRMs and opioids. Although the SHCP indicated having knowledge of PRMs, the knowledge stemmed from sources other than formalized education, calling into question where their sources of knowledge come from. Enhancing their knowledge in these areas is vital for supporting athletes and addressing their pain management needs safely and effectively. Additionally, the study emphasizes the importance of SHCPs staying updated with federal and provincial laws and regulations to ensure they remain within the boundaries of their professional scope of practice.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".