Coping resources and strategies of Canadian ice-hockey players: An empirical National Hockey League career model
Bibliographic record
Abstract
Sport psychology researchers have studied careers of Canadian ice hockey players in the National Hockey League (NHL) and devised an empirical NHL career model. The model was comprised of career stages, statuses, demands and barriers to career progression without any indication of coping. The intent in the present article is to feature coping resources and strategies utilized by players during each status and career stage within the empirical model. Five rookies, 5 veterans, and 13 retirees participated in conversational interviews and the data underwent a deductive thematic analysis. Prospects seeking to gain entry into the NHL set controllable expectations rather than playing to impress coaches and staff. Most prospects played in the minor leagues where they adjusted their expectations to accept roles that they were likely to have during an NHL call-up. The career stage of developing as an NHL player was about rookies producing immediately in their role while holding off internal competition for their roster spot. In the same stage, sophomores were in their second full NHL season and they studied their opponents to avoid the sophomore slump. The stage of reaching the NHL elite involved constant pressure for point production and winning playoff games. The final stage was about seasoned veterans maintaining NHL play involvement by preserving their physique despite being worn down from long careers in a contact sport. The authors will discuss the significance of the model for sport psychology researchers and practitioners, and NHL stakeholders.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".