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Record W4409795574 · doi:10.1123/iscj.2024-0103

Design Thinking: Innovative Knowledge Co-Creation in Coaching for Para Sport

2025· article· en· W4409795574 on OpenAlexaff
Rabia Ozturk Kizilkaya, Diane M. Culver, Koray Kılıç, Timothy Konoval

Bibliographic record

VenueInternational Sport Coaching Journal · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsUniversity of AlbertaUniversity of Ottawa
Fundersnot available
KeywordsCoachingKnowledge creationKnowledge managementDesign thinkingCo-creationBusinessPsychologyComputer scienceHuman–computer interactionMarketing

Abstract

fetched live from OpenAlex

The increasing participation in Para sport has created a demand for more qualified coaches, yet their development mainly depends on informal and experiential methods due to limited formal opportunities. Effective collaboration between sports organizations and governmental bodies is crucial to developing comprehensive coach development opportunities tailored to the specific needs of Para sports. Design Thinking offers a viable solution to bridge existing gaps by fostering innovative, human-centered approaches that lead to the development of solutions tailored to the evolving demands of Para sport coaching. The purpose of this study was to introduce and propose the utilization of Design Thinking to enhance coach development opportunities in Para sport, illustrated through a comprehensive analysis of a 2-day design challenge event based on the Hasso-Plattner Institute Model. The event brought together a varied group of stakeholders, including managers, coaches, coach developers, and athletes. Design Thinking, with its focus on empathy, iterative problem-solving, and collaboration, was instrumental in helping participants move beyond their individual biases. The study provided actionable strategies for implementing Design Thinking in the context of Para sports, contributing to improving the overall Para sport experience.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.660
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.530
Teacher spread0.413 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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