Therapeutic Bodily Assistive Devices and Paralympic Athlete Expectations in Winter Sport
Bibliographic record
Abstract
OBJECTIVE: To ascertain the impact of therapeutic bodily assistive devices that enable beyond-the-normal body abilities on sport in general and the Paralympics and Olympics in particular. DESIGN: Cross-sectional survey. SETTING: Online. PARTICIPANTS: Members of the National Council on Rehabilitation Education (United States). Distribution of online survey link to membership. ASSESSMENT OF RISK FACTORS: The survey used a combination of 37 simple yes or no, Likert scale, and opinion rating scale questions. This article is based on 4 of the 37 questions that focus on the impact of therapeutic enhancements on various aspects of sport. MAIN OUTCOME MEASURES: Whether respondents felt that there is an impact of therapeutic bodily assistive devices that enable beyond-the-normal body abilities on the participation of people with disabilities in sport of all levels and the self-identity of athletes with disabilities. Secondary outcome measure was what the respondents felt the impact may be. RESULTS: The respondents indicated that therapeutic bodily assistive devices, which enable beyond-the-normal body abilities, will have an impact on participation of people with disabilities in sport at all levels and on the self-identity of athletes with disabilities. CONCLUSIONS: Given the result that the respondents felt that therapeutic enhancements will impact various aspects of sport, it may be prudent to initiate a broader discourse around therapeutic enhancement and to revise codes of ethics so that they give guidance on this topic.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".