24 Community provider perspectives on an autism learning health network: A qualitative study
Bibliographic record
Abstract
Abstract Background Although autism is highly prevalent, no single care centre has a sufficient number of patients to produce generalizable knowledge; this slows the pace of quality improvement research. Learning health networks (LHNs) offer a promising solution to improve the quality and efficiency of healthcare by integrating clinical and research data. The Autism Care Network (ACNet) is an LHN composed of 20 clinics across the United States and Canada dedicated to developing the most effective approach to care for children with autism. In Canada, the majority of autistic patients receive a majority of their ongoing care in the community. Therefore, to make meaningful improvements in the continuum of care for patients and their families, it is essential that the ACNet be extended to include community physicians. Objectives The primary objectives were to (1) understand the current data collection practices, learning needs, clinic capacity, areas of improvement, and overall interest of community physicians in participating in a LHN and (2) identify community physicians’ perspectives on the benefits and disadvantages of participating in a LHN and ways in which their engagement in a LHN can be supported. Design/Methods In-depth semi-structured interviews were conducted participants who were purposively sampled from community providers who had previously participated in autism-focused educational programming. The interview guide was developed by the three ACNet sites based on experience with LHNs and working with community physicians. Data were analyzed using an interpretative phenomenological analysis approach with a social constructivist paradigm. Results Analysis of 29 interviews identified 5 primary themes: (1) “Navigating Administrative Challenges,” which highlighted the lack of time, resources, and administrative capacity in the community; (2) “Improving Data Collection Practices”, which emphasized the barriers to consistently collecting information from patients and families; (3) “Increasing Provider Confidence and Competence,” which explored the challenges of navigating the everchanging landscape of community autism care; (4) “Breaking Down Silos”, which focused on fragmented care and the lack of communication between allied health resources, diagnostic hubs, and community physicians; and (5) “Systemic and Societal Barriers to Achieving Best Practices,” which explored the systemic barriers that impact autism care for physicians and families. Conclusion This study provides an understanding of the experiences of community physicians regarding the challenges of community-based autism care. LHNs have the potential to address several of the issues highlighted by community physicians in autism care. Additionally, LHNs can play an important role in improving community care in fields beyond autism.
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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 | 0.016 | 0.025 |
| 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 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".