Assessing impacts on career human agency and counselling needs of professional athletes during a pandemic
Bibliographic record
Abstract
Professional athletes in Canada are working to recover their careers after the disruptions caused by the coronavirus (COVID-19) pandemic. As workforces shut down to promote social distancing to curb the spread of disease, athletes’ careers and personal lives were drastically impacted. The current study aimed to understand the needs and challenges faced by the professional athlete population during the pandemic to inform their career recovery. A lens from career human agency theory (CHAT) was applied to provide a framework for this exploration, focusing on how its dimensions—career intentionality, career forethought, career self-reactiveness, and career self-reflectiveness—were impacted throughout the pandemic. This was investigated through a qualitative semi-structured interview procedure involving ten participants, followed by an interpretative phenomenological analysis. Themes derived from the analysis focused on understanding athletes’ career intentions and the meaning of sports in their lives, how their goals were disrupted by pandemic challenges, ways athletes reacted and coped, and what was learned to adapt and move forward. A career human agency theory for athlete career disruption (CHAT-ACD) model was developed to guide counsellors in the treatment of professional athletes as they work on career recovery and inform methods of support for possible future career disruptions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".