Community Provider Perspectives on an Autism Learning Health Network: A Qualitative Study
Bibliographic record
Abstract
Although autism is highly prevalent, no single care center has enough patients to produce generalizable knowledge of optimal care; this slows the pace of quality improvement research. The Autism Care Network (ACNet) is a learning health network (LHN) dedicated to developing the most effective approach to care for autistic children and adolescents through integrating clinical and research data. Given that most autistic patients receive care in the community, expanding ACNet to include community providers is essential to improve autism care. Our objectives were to: (1) understand the current data collection practices, learning needs, capacity, and overall interest of community clinicians in participating in an autism LHN; (2) identify their perspectives on participating in a LHN and ways in which their engagement and interest can be cultivated. Participants were purposively sampled from community physicians who participated in ASD-focused educational programming. In-depth semi-structured interviews were conducted. Analysis of 29 participant interviews yielded five primary themes: Navigating Administrative Challenges, Improving Data Collection Practices, Increasing Provider Confidence and Competence, Breaking Down Silos, and System and Societal Barriers to Achieving Best Practices. This study provides a rich and nuanced understanding of the experiences of community providers regarding the challenges of ASD care provision in the community. Overall, these findings suggest that LHNs have the potential to address several of the issues in community autism care highlighted by community providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".