Maximizing the use of Special Olympics International's Healthy Athletes database: A call to action
Bibliographic record
Abstract
There is a critical need for high-quality population-level data related to the health of individuals with intellectual disabilities. For more than 15 years Special Olympics International has been conducting free Healthy Athletes screenings at local, national and international events. The Healthy Athletes database is the largest known international database specifically on the health of people with intellectual disabilities; however, it is relatively under-utilized by the research community. A consensus meeting with two dozen North American researchers, stakeholders, clinicians and policymakers took place in Toronto, Canada. The purpose of the meeting was to: 1) establish the perceived utility of the database, and 2) to identify and prioritize 3-5 specific priorities related to using the Healthy Athletes database to promote the health of individuals with intellectual disabilities. There was unanimous agreement from the meeting participants that this database represents an immense opportunity both from the data already collected, and data that will be collected in the future. The 3 top priorities for the database were deemed to be: 1) establish the representativeness of data collected on Special Olympics athletes compared to the general population with intellectual disabilities, 2) create a scientific advisory group for Special Olympics International, and 3) use the data to improve Special Olympics programs around the world. The Special Olympics Healthy Athletes database includes data not found in any other source and should be used, in partnership with Special Olympics International, by researchers to significantly increase our knowledge and understanding of the health of individuals with intellectual disabilities.
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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 | 0.465 | 0.527 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.029 | 0.048 |
| Open science | 0.023 | 0.029 |
| Research integrity | 0.021 | 0.029 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".