Quality participation in parasport: Integrating evidence and community insights to develop a conceptualization of optimal parasport experiences
Bibliographic record
Abstract
Through parasport, adults and young athletes with physical impairments often report increases in well-being and related psychosocial perceptions compared to non-athletes. While researchers often attribute these benefits to social participation that goes-along with parasport, few existing frameworks comprehensively describe the components of optimal parasport experiences. We sought to generate an evidence-informed conceptualization of optimal parasport participation, using a view of participation across disability settings that highlights six experiential elements (Belongingness, Autonomy, Challenge, Mastery, Engagement and Meaning; Martin-Ginis, Evans, Mortenson, & Noreau, 2016). We integrated existing literature and stakeholder input within a three-phase process based on AGREE-II guideline development methods. In Phase One, we formed propositions about optimal experiences based on insights from systematic reviews and qualitative studies. In Phase Two, we developed a provisional conceptualization informed by an expert round-table and an online descriptive survey with 80 parasport athletes, parents, coaches, and administrators. In Phase Three, we refined the conceptualization using an online expert panel with researchers and sport administrators (n = 20). As a result, an initial list of quality elements grew to a conceptualization that uniquely defines the six elements in ways that represent the parasport context. We also identified 27 optimal conditions that may promote quality experiences across the physical (e.g., accessibility) and social environment (e.g., coach-athlete communication), as well as sport activities (e.g., safety). This conceptualization provides direction for future research and is a foundation that parasport organizations may apply through tools to enhance participation in their unique contexts (e.g., developing guidelines for sport programs).
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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 | 0.062 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.003 | 0.004 |
| 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".