The Exploration, Ideation, and Design of Athlete Retirement Prototypes for the Canadian High-Performance Sport System
Bibliographic record
Abstract
High-performance athletes' (HPAs) engagement in proactive retirement preparation has become increasingly important for their well-being and quality of life during and after sport. As researchers continue to highlight the plethora of challenges underprepared HPAs face when retiring from sport, retirement support available to Canadian HPAs through Game Plan continues to be significantly underutilized (Game Plan, 2023). Researchers have identified environmental barriers as an influencing factor determining athletes' ability to access and utilize available forms of retirement support (Brassard et al., 2022). Current literature lacks the use of a holistic lens to identify the organizational factors that act as barriers and facilitators faced by athletes in this regard. An exploration with this lens could enhance our understanding of HPAs' ability to prepare for retirement by providing insights into the organizational interplay sport members have in facilitating a sport environment that enables, restricts, or hinders a HPA's ability to prepare for life after sport. Furthermore, a holistic lens can provide researchers with the necessary information to develop further frameworks and strategies to minimize athlete retirement barriers (Stambulova et al., 2020). The purpose of this doctoral dissertation is to address this gap through the following objectives: (a) examine how active and retired athletes utilized retirement support mechanisms, (b) explore high-performance sport members' perspectives and understanding of retirement support mechanisms, and (c) facilitate co-design sessions to innovate solution-driven prototypes to address sport members' athlete retirement needs. The methodologies of Bronfenbrenner's Ecological Systems theory and Design Thinking were adopted to guide and facilitate this research. This dissertation is composed of three articles, each correlating with the research objectives. With the first research objective, a secondary data analysis was used to examine how athletes in the Canadian sport system were utilizing Game Plan's resources from 2019-2021. This examination provided insight into athletes' Game Plan usage discrepancies when drawing comparisons to the funding each National Sport Organization (NSO) earned, their league of competition (e.g., Para or Non-Para sport), and repeat uses of Game Plan's resources after an initial interaction. As a result of the findings from this examination, considerations are proposed for sport organizations who seek to improve their athlete engagement with Game Plan's retirement resources. The second objective was investigated through empathy interviews (Kelley & Kelley, 2013) conducted with active athletes, retired athletes, support personnel, and performance partners across the sport system. A total of 19 individuals were interviewed. Empathy interviews were used to create personas for each role. Findings from this exploration add to the literature by outlining the following empirical findings: (a) high-performance sport members have limited knowledge and understanding of accessible retirement resources, (b) sport organizations' cultures can present barriers or facilitators through direct and indirect messages to athletes, and (c) HPAs who proactively sought out support or had a good sport-life balance experienced fewer challenges adapting to life after sport. The third and final objective was supported via the Stanford d. school's five-step Design Thinking model: empathize, define, ideate, prototype, and test. This section of research consisted of a co-design session conducted with two groups of participants. In each session, participants were led through brainstorming cluster activities to define, ideate, design, and evaluate athlete retirement solution-driven prototypes. Sport members collaboratively designed the following three evidenced-based prototypes: (a) creating a new funding metrics system, (b) strengthening collaboration between NSOs and Game Plan, and (c) enhancing athlete retirement coach education. The Design Thinking process as well as the insights outlining why and how the adoption of each prototype can advance HPAs' ability to prepare for their lives after sport are discussed. This article is the first to adopt a Design Thinking approach to advance athlete retirement literature and aims to serve as a catalyst for researchers to adopt Design Thinking methods to further the field of sport performance psychology. The results of this doctoral dissertation research add empirical, theoretical, and methodological contributions to both the athlete retirement and Design Thinking literature.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".