Updates to the Autism Intervention Research Network on Physical Health (AIR-P) Research Agenda
Bibliographic record
Abstract
INTRODUCTION: Autistic individuals, now representing one in 36 individuals in the U.S., experience disproportionate physical health challenges relative to non-autistic individuals. The Health Resources and Services Administration's (HRSA) Autism Intervention Research Network on Physical Health (AIR-P) is an interdisciplinary, multi-center Research Network that aims to increase the health, well-being, and quality of life of autistic individuals. The current paper builds on the initial AIR-P Research Agenda (proposed in Year 1) and provides an updated vision for the Network. METHODS: Updates to the Research Agenda were made via the administration of a Qualtrics survey, and disseminated widely to all AIR-P entities, including the Research Node Leaders, Steering Committee, Autistic Researcher Review Board, and collaborating academic and non-academic entities. Network members were tasked with evaluating the Year 1 Research Agenda and proposing additional priorities. RESULTS: Within each Research Node, all Year 1 priorities were endorsed as continued priorities for research on autism and physical health. Specific topics, including co-occurring conditions and self-determination, advocacy, and decision-making, were particularly endorsed. Opportunities for exploratory studies and intervention research were identified across Research Nodes. Qualitative responses providing feedback on additional research priorities were collected. CONCLUSION: The updated AIR-P Research Agenda represents an important step toward enacting large-scale health promotion efforts for autistic individuals across the lifespan. This updated agenda builds on efforts to catalyze autism research in historically underrepresented topic areas while adopting a neurodiversity-oriented approach to health promotion.
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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 | 0.280 | 0.328 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".