Coping with injury and daily stressors in university student athletes
Bibliographic record
Abstract
While recent research has generated a great deal of useful information about the nature of the stressors facing injured athletes and the coping strategies used during injury rehabilitation, few studies have examined the actual experiences of injured student athletes.This study sought to begin to address this gap in the literature by exploring the stress and coping experiences of injured student athletes over the course of their rehabilitation.Nine university student athletes with athletic injuries were recruited to complete fourteen consecutive weekly journal entries describing their stressors and coping strategies related to the injury rehabilitation process and other areas of life.Five participants (three female and two male) provided full journal datasets and then completed semi-structured interviews after returning to sport.Grounded theory methodology was utilized to analyze the journal and interview data.Themes arose related to the student athlete lifestyle, stressors, psychological responses to injury, coping strategies and coping effects, coping processes and perceived benefits.The results are discussed within the context of models of sport injury rehabilitation and previous research on stress and coping with athletic injury.The study identified several stressors and coping strategies specific to injured student athletes.These include balancing intensive time demands, which became further strained with the addition of rehabilitation, the effect of the injury on employment, and related coping strategies.Strengths and limitations of the study are addressed, and recommendations for future research are made with respect to this specific population and, more generally, research on stress and coping with athletic injury.Recommendations regarding strategies to support injured student athletes are also offered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".