Design Thinking for Research in Sport Psychology
Bibliographic record
Abstract
This presentation showcases Design Thinking (DT) in research. Although DT has long been used in certain fields (e.g., architecture, engineering), its use in the sports domain is nascent. DT, a paradigm, methodology, and method, encourages creative, multi-disciplinary and multi-stakeholder teams to use a systematic and collaborative approach to identify and creatively solve problems with abductive reasoning, which helps to understand field-deep knowledge (Chamber et al., 2021). DT aims to solve wicked problems in a human-centred, desirable, technologically feasible, and economically viable way to ensure innovation and change are sustained over time. DT can help with the system change (e.g., sport psychology, coach education). In our first case, we organized the National Coaching for Para Sport Summit based on the DT paradigm (wicked problem: more inclusive education for coaching in Para sport), methodology (Hasso-Plattner Institution model; HPI) and methods (empathy mapping, fictional personas). Second, using DT methodologically, we explored Canadian high-performance athlete retirement support mechanisms. We used the 5-stage HPI process to conduct empathy interviews, which we analyzed abductively, creating personas to be used to ideate solutions. Third, we used DT as a paradigm in a case study of student-athlete mental health at uOttawa. Data were generated using DT tools (e.g., enabler interviews, digital storytelling, empathy mapping) resulting in a stakeholder map and fictional personas. We recommend DT as a promising concept whether as a paradigm, methodology, and/or method for sport psychology research aimed at the re-imagination of complex problems from a holistic perspective, considering the realities of end-users.
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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 | 0.064 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".