The Autism Intervention Research Network on Physical Health (AIR-P) Research Agenda
Bibliographic record
Abstract
OBJECTIVES: In the United States, autistic individuals experience disproportionate physical and mental health challenges relative to non-autistic individuals, including higher rates of co-occurring and chronic conditions and lower physical, social, and psychological health-related quality of life. The Autism Intervention Research Network on Physical Health (AIR-P) is an interdisciplinary, multicenter research network for scientific collaboration and infrastructure that aims to increase the life expectancy and quality of life for autistic individuals, with a focus on underserved or vulnerable populations. The current paper describes the development of the AIR-P Research Agenda. METHODS: Development of the research agenda involved an iterative and collaborative process between the AIR-P Advisory Board, Steering Committee, and Autistic Researcher Review Board. The methodology consisted of 3 phases: (1) ideation and design, (2) literature review and synthesis; and (3) network engagement. RESULTS: Six core research priorities related to the health of autistic individuals were identified: (1) primary care services and quality, (2) community-based lifestyle interventions, (3) health systems and services, (4) gender, sexuality, and reproductive health, (5) neurology, and (6) genetics. Specific topics within each of these priorities were identified. Four cross-cutting research priorities were also identified: (1) neurodiversity-oriented care, (2) facilitating developmental transitions, (3) methodologically rigorous intervention studies, and (4) addressing health disparities. CONCLUSIONS: The AIR-P Research Agenda represents an important step forward for enacting large-scale health-promotion efforts for autistic individuals across the lifespan. This agenda will 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 | 0.244 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".