Understanding the psychiatric effects of concussion on constructed identity in hockey players: Implications for health professionals
Bibliographic record
Abstract
OBJECTIVE: The following study was undertaken to investigate the effect of concussion and psychiatric illness on athletes and their caregivers. METHODS: Semi-structured interviews with 20 ice hockey stakeholders (17 men and 3 women) including minor and professional players, coaches, parents, and physicians were conducted over two years (2012-2014). These interviews were analyzed using grounded theory. RESULTS: From this analysis, a common biographical theme emerged whereby the subject's identity as a hockey player, constructed early in life over many years, was disrupted by concussion. Furthermore, some players underwent a biographical deconstruction when they experienced post-concussive mental illness, which was amplified by isolation, stigma from peers, and lack of a clear life trajectory. Many players obtained support from family and peers and were able to recover, as evidenced by the biographical reconstruction of their identity post-hockey concussion. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Understanding the process of biographical deconstruction and reconstruction has significant psychosocial treatment implications for both healthcare professionals and caregivers of this population. Specifically, the authors suggest that interpersonal psychotherapy (IPT) that focuses on role transitions may create opportunities to facilitate the process of biographical reconstruction and life transition.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".