Concussion beliefs in varsity athletes: Identifying the good, the bad and the ugly
Bibliographic record
Abstract
Objective Identify and describe attitudes and intentions towards personal concussion risk and protective behaviours among varsity athletes. Determine subgroups of athletes characterized by problematic intentions towards concussion prevention and management behaviours. Design Cross-sectional survey. Main outcome measures Varsity athletes ( N = 175; 60% male; 55.4% contact athletes; 56.6% history of concussion) completed a survey examining attitudes and intentions towards personal risk and concussion-management behaviours. Cluster and discriminant analyses were used to identify athlete risk response subgroups on intention items. The clusters were examined for differences in attitudes towards concussion prevention behaviours, demographics and concussion exposure. Results A substantially problematic subgroup of athletes (28% of the sample) reported low intent to engage in post concussion management practices or primary prevention behaviours. These individuals reported high concussion-risk acceptance and very low belief in the efficacy of concussion-management behaviours. They were also more likely to have sustained a concussion. Two other clusters demonstrated more acceptable behavioural intentions towards concussion prevention and management, with one holding model attitudes and intentions. Conclusions Varsity athletes exhibit one of three different patterns of intentions and attitudes towards concussion prevention and management behaviours. Athletes in one of these groups are at much greater risk of concussion injury and poorly follow recommended treatments. Intervention programmes need to target and aim to change these problematic intentions and attitudes to improve the effectiveness of concussion prevention and injury management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".