Persistent Psycho-affective Alterations In Elite Adolescent Hockey Players With A History Of Concussion
Bibliographic record
Abstract
BACKGROUND: Concussive injuries are an increasing public health concern; however, the majority of research focuses on neuropsychological outcomes in adults, with less attention given to developing populations and psycho-affective outcomes. Furthermore, symptoms of anxiety and depression are too often neglected when it comes to the return-to-play decision. PURPOSE: To determine the influence of concussive injuries on psycho-affective health in elite adolescent hockey players. METHODS: Results from forty-nine elite hockey players (28 concussed, 21 non-concussed) from the Québec Midget AAA hockey league (age 14-17) were analyzed in the current study. Athletes completed a psychoaffective assessment consisting of the Depression (BDI) and Anxiety Inventories (BAI) of the Beck Youth Inventory (BYI). Independent t-tests were used to analyze the raw data and t-scores for both the BAI and BDI. Bivariate correlations were carried out to measure the relations between injury variables and metrics of psycho-affective health. RESULTS: Both the raw and t-scores for the BAI were significantly greater in concussed hockey players versus their non-concussed peers (Raw score: mean = 9.50 vs. 6.24; p = .04 / t-score: mean = 47.86 vs. 44.19; p = .03). In addition, analysis revealed a group trend for the BDI raw scores and t-score (Raw score: mean = 5.68 vs. 3.38; p = .09 / t-score: mean = 46.68 vs. 44.10; p = .06). However, bivariate correlations failed to reveal any significant relations between injury variables and metrics of psycho-affective health relationship (ps ≥ .39 for all). CONCLUSION: Elite level adolescent hockey players who report to be symptom free on concussion checklists and are actively engaged in their sport still exhibit increased depression and anxiety relative to their non-injured teammates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".