Influence of impairment type on the development of competitive athletes with a physical disability
Bibliographic record
Abstract
The developmental trajectories of athletes with physical disabilities are complex — and when it comes to understanding the factors that influence their development in sport, existing research has only scratched the surface. To deepen our understanding, this study examined the influence of impairment type on (a) how athletes were introduced to sport, (b) the age at which nine sport-related developmental milestones (e.g., first participated in offseason training) were achieved, and (c) the time it took to reach each milestone. An international sample of competitive athletes with physical impairments (N = 187; 68% male; Mage = 33) provided training histories using the Developmental History of Athletes Questionnaire. Participants were divided into four groups: spinal cord injuries (SCI; n = 67), amputations and/or limb deficiencies (A/LD; n = 48), cerebral palsy and/or spina bifida (CP/SB; n = 32), and other physical impairments (OPI; n = 40). Participants with SCI were most frequently introduced to sport through healthcare professionals (54%), A/LD through friends or relatives (26%), and CP/SB through talent search programs (22%). Separate one-way ANOVAs revealed that the SCI group was significantly older than the other three groups at eight of the nine milestones (p < .05). However, significant differences persisted for only three milestones in terms of time. Specifically, participants with SCI took significantly less time than participants with OPI to reach these three milestones (p < .01). Thus, despite differences in age and how they got involved, athletes with distinct physical impairments appear to progress through sport at a similar rate.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".