The head or the heart: Emotions and cognitions influence on concussion prevention and management behaviors
Bibliographic record
Abstract
Concussions are a common athletic injury. Considerable research, time, and resources have been dedicated to developing educational resources to encourage athletes to engage in concussion prevention and management behaviors. Much of the messaging is based on fear appeals of the consequences of concussion injury, yet the relationship between cognitive and emotional representations of concussion injury and the enactment of protective behaviours is unclear. Our aim was to investigate the dual-process relationship of cognitive components and emotional components of risk representation on concussion protective behaviors. We use regression and mediation analysis of two surveys of varsity athletes (N1 = 175, N2 = 142) utilizing standardized measures of injury representations and a novel imagery measure. Cognitive representations including perceived effects and control over concussion injury returned as significant predictors of protective intentions and behaviours. Emotional representations of injury did not significantly predict behaviour and did not mediate the relationship between cognitions and intentions. Imagery measures still showed strong negative affect connected to concussion injury. From this study we see that emotions are a component of concussion injury, but their role in prompting behaviour is unclear. We should consider the best methods to capture emotional representation around injury for future studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".