Reframing the Service Environment in Collegiate Sport: A Transformative Sport Service Research Approach
Bibliographic record
Abstract
This study applies a transformative sport service research approach to examine student-athletes’ wellness within a collegiate sport setting. Sixteen semi-structured interviews were completed during the COVID-19 pandemic and the stop of play in Canada (early 2021). Findings denoted wellness was influenced by this time period as well as organizational factors which are within the purview of existing management practices. The study details the service environment to understand physical and mental well-being while taking into consideration the general and organizational environments which influence the student-athletes. Findings demonstrate that eudaimonic-related experiences (e.g., learning, development, relationship) are related to student-athletes' mental well-being. This research underpins the role of education, policies, and communication, which draws several implications for the service environment in a collegiate sport setting and the key stakeholders involved in producing an environment to enhance student-athletes’ experience. The paper elaborates on the importance of the service environment and provides evidence of what student-athletes suggest management can change and focus their efforts on towards creating a transformative service environment. Theoretical implications for the transformative service research are put forward, including the co-creative aspects to determine programming which could contribute towards student-athletes wellness. Broader suggestions for change within the sport system and future research are also advanced.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".