Chiropractic Care in a Multidisciplinary Sports Health Care Environment: A Survey of Canadian National Team Athletes
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to describe the utilization of health team practitioners among national-level athletes and report their injury profile as well as access to and knowledge of chiropractic care. METHODS: This study was a retrospective analysis of non-identifiable, cross-sectional survey data that were previously collected from members of the AthletesCAN organization who voluntarily completed a web-based, bilingual survey in July or August 2017. The sport of the athlete, number of years on a national team, number and type of injuries, health care practitioners visited, and specific details on chiropractic care were collected. Descriptive statistics were performed to summarize the responses in terms of frequencies and percentages. RESULTS: There was an 11% response rate (198/1733), with 67 unique sports identified (21 winter sports [50 athletes] and 46 summer sports [148 athletes]). Athletics and swimming were the sports with the most respondents. Fewer than half (43.9%) of the 198 respondents were members of AthletesCAN for 2 to 5 years. Seventy percent reported 1 to 5 injuries over their career, with ankle, low back, and shoulder the most likely body parts affected. A majority of athletes (93.4%) reported visiting multiple practitioners, including medical physicians, physiotherapists, athletic therapists, massage therapists, and chiropractors. Two-thirds (67%) of athletes sought chiropractic treatment, most typically for neck or back pain (81.3%), despite nearly half (45.7%) being unsure about access to chiropractic care. CONCLUSION: This sample of Canadian national team athletes who experience an injury may seek care from multiple types of health care providers and include chiropractic as part of their approach to health care.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".