Recognizing the Symptoms of Mental Illness following Concussions in the Sports Community: A Need for Improvement
Bibliographic record
Abstract
OBJECTIVE: To evaluate the awareness of concussion-related symptoms amongst members of the sports community in Canada. METHODS: A cross-sectional national electronic survey was conducted. Youth athletes, parents, coaches and medical professionals across Canada were recruited through mailing lists from sports-related opt-in marketing databases. Participants were asked to identify, from a list of options, the symptoms of a concussion. The proportion of identified symptoms (categorized as physical, cognitive, mental health-related and overall) as well as participant factors associated with symptom recognition were analyzed. RESULTS: The survey elicited 6,937 responses. Most of the respondents (92.1%) completed the English language survey, were male (57.7%), 35-54 years of age (61.7%), with post-secondary education (58.2%), or high reported yearly household income (>$80,000; 53.0%). There were respondents from all provinces and territories with the majority of respondents from Ontario (35.2%) or British Columbia (19.1%). While participants identified most of the physical (mean = 84.2% of symptoms) and cognitive (mean = 91.2% of symptoms), they on average only identified 53.5% of the mental health-related symptoms of concussions. Respondents who were older, with higher education and household income, or resided in the Northwest Territories or Alberta identified significantly more of the mental health-related symptoms listed. INTERPRETATION: While Canadian youth athletes, parents, coaches and medical professionals are able to identify most of the physical and cognitive symptoms associated with concussion, identification of mental health-related symptoms of concussion is still lagging.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".