Building a roadmap for applied sport psychology research: Understanding the gaps for research and innovation within the Canadian Olympic and paralympic sport institute network
Bibliographic record
Abstract
The Canadian Olympic and Paralympic Sport Institute network (COPSIN) seeks to provide a world-class, multi-sport daily training environment for athletes and coaches through expert leadership, services, and programs. Along with world leading applied service provision, sport psychology practitioners are mandated to align service delivery closely with performance-led research that aims to better understand specific issues and achieve breakthroughs in training science, maximizing competition performance, and improving athlete mental health and wellbeing. The purpose of this presentation is to share and reflect on the author's opportunities and challenges to meet this mandate as a contracted employee over the period of six years within COPSIN. In particular, this presentation will highlight the challenges to carrying out research and innovation (R & I) activities as it relates to financial compensation and funding, practitioner role clarity, and the scope of practice including the development of collaborative relationships for research within the sport science team. Additionally, ethical considerations for action research by applied practitioners working in the high performance environment will be discussed. The intent is to provide University-based colleagues with a clear and unambiguous understanding of the gaps that exist within the current applied sport psychology R & I landscape so that partnership-based strategies can be identified and established that are mutually beneficial.
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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 | 0.193 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.048 | 0.034 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.023 | 0.037 |
| Insufficient payload (model declined to judge) | 0.014 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".