Development of a concussion perceptions questionnaire
Bibliographic record
Abstract
The prevalence and consequences of concussion injury have given impetus to efforts to curb concussion injury and reduce long-term effects through public awareness and education campaigns directed at concussion beliefs. The field lacks a standardized instrument to measure concussion beliefs. We report on the development of a concussion perceptions questionnaire (CPQ). We base the CPQ on the Common Sense Model (CSM) of Illness Representation. The CSM is a systems approach to self-regulation, which posits that processing of internal and external stimuli prompt mental and emotional representations of health that guide coping and prevention behaviours, which are then appraised relative to valued outcomes. Items were modified from the Illness Perceptions Questionnaire - Revised. We validated the CPQ using attitudes and intentions items towards protective behaviour derived from a review of the literature, concussion programs and the authors’ personal experience and knowledge of concussion injury in sporting contexts. To examine scale structure, we administered items (38) to a sample of 243 undergraduate students (20y, 87% women, 28% athletes, 34% concussion exposure). We validated the scale with items examining attitudes and intentions toward prevention behaviours. We used principal components analysis to examine scale structure, item loading and model fit. Reliability coefficients were subsequently generated for identified subscales. Analysis yielded seven subscales consistent with the dimensions identified in previous research (personal and treatment control, timeline, cyclicality, understanding, consequences, emotions) accounting for 67 percent of the variance. Item loadings (all > .56) and communalities (all >.44) were satisfactory. Cronbach’s alpha was acceptable (>.71) for all but the personal control subscale (.61). Factor scores were related to attitudes and intentions towards protective behaviours in predictable ways. The CPQ provides a theoretical informed, empirically sound and efficient base for conducting research and evaluating concussion beliefs.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".