Impact of COVID-19 Sport Cancelations on the Self-Identity and Psychological Distress of High School Student-Athletes
Bibliographic record
Abstract
The stay-at-home measures enacted during the COVID-19 pandemic led to sudden changes in the lives of individuals worldwide. For high school student-athletes, these changes meant transitioning to online schooling, heavily reducing their social activities, and enduring the cancelation of sport activities. Scholars have expressed concerns related to the potential consequences of these changes on adolescents’ self-identity and psychological distress. The purpose of the present study was to qualitatively explore how the changes induced by the COVID-19 pandemic affected high school student-athletes’ self-identity and psychological distress. Twenty-two Canadian high school student-athletes were interviewed using a semi-structured interview format. Transcripts were subjected to a reflexive thematic analysis, leading to the creation of four central themes: (a) Pre-COVID Identity; (b) COVID Identity Confusion; (c) COVID Psychosocial Distress; and (d) Learning to Dance in the COVID Rain. This study sheds light on the pandemic-related experiences of high school student-athletes in relation to sport cancelation measures and provides insights into how stay-at-home restrictions impacted self-identity and psychological distress levels. These results can help inform interventions aimed at supporting the well-being of high school student-athletes now that school sport programs have resumed operations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".