Understanding Post-Career adjustment in Ex-Professional Ice Hockey Enforcers: Concussion history and chronic pain
Bibliographic record
Abstract
: Media reports of difficulties with post-career functioning and death in ex-professional hockey enforcers have led to concerns within the ice hockey community. The purpose of the study was to interview 10 ex-professional ice hockey enforcers and integrate their lived experiences into the narrative on post-retirement problems experienced by these athletes. Based on the existing literature, it was hypothesised that ex-professional hockey enforcers would be at high risk for development of symptomology consistent with Chronic Traumatic Encephalopathy (CTE). A mixed methods analytical approach informed by Pragmatic and Indigenous methodologies was employed. Participants had a significant history of fighting in their sport (range 100–250; mean = 218.5). All had significant concussion histories related to their careers in hockey. One participant reported problems post-career associated with concussions sustained while playing hockey. Five participants reported issues with chronic pain that mildly impacted their sleep and/or daily functioning. The majority reported relatively good post-career functioning. In summary, the hypothesis that ex-professional hockey enforcers are at high risk for developing symptomology consistent with CTE was not supported. The pattern of results is in opposition to the commonly held perspective that fighting in hockey leads to a cascade of events that results in poor post-career outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".