Characterizing Project ECHO Autism Case Recommendations and Implementation
Bibliographic record
Abstract
Background: The Extension for Community Health Outcomes (ECHO) model creates virtual communities to help mitigate the barriers to treating complex conditions, including autism spectrum disorder (ASD; autism). ECHO Ontario Autism was developed to build province-wide capacity to diagnose autism by building community providers' skills; and 2) to improve provider understanding and confidence around autism and available therapies and supports. ECHO Ontario Autism attempts to achieve these goals through multipoint video conferencing that connects community providers with peers and specialists who can offer education, guidance on cases, and support. This study aims to determine the types of recommendations provided through the ECHO Autism Ontario program, identify reasons some recommendations were not enacted, and summarize how recommendations have impacted the practice of clinicians who present cases within ECHO didactic sessions. Approach: To code the types of recommendations provided within the ECHO Ontario Autism cases, two researchers coded deductively with a pre-existing coding guide from a previous evaluation of this program. Two researchers used an inductive approach to developing codes to categorize reasons recommendations were rejected and the impact of ECHO Ontario Autism. A summative content analysis was used to determine the frequencies with which these categories occurred. The coding guide and categories were reviewed with the broader team at regular meetings. Results: The final analysis included a total of 32 cases presented by 8 individuals, culminating in 289 recommendations. Across the 32 cases, 74% of recommendations were implemented (n = 24). This[MP] study emphasized the importance of resources in autism care, finding that accessing community resources and resources and tools for further learning were the two most common categories of recommendations, with implementation rates over 75%. While all implementation rates were generally high, recommendations that were not enacted were most often not due to reasons relating to the child/family, including the child or family declining the recommendation, the family seeking alternative resources, and the provider feeling that the family was not ready for the recommendation. This study also summarized the impact of ECHO Ontario Autism on clinical practice. Providers indicated that ECHO Ontario Autism positively influenced their approach to care for the relevant case, impacted their practice broadly beyond their ECHO cases, increased their diagnostic capabilities, and provided interpersonal benefits both with families and colleagues. Implications: This information will help to increase the utility of the recommendations provided in the ECHO Ontario Autism program and provide broader insights into barriers and facilitators of community-based autism practice. Particularly, this research emphasizes the necessity for autism-care recommendations to be relevant and well-explained to families by physicians in order for them to be successfully implemented. Additionally, this research points to barriers to care, including financial and access barriers, that must be mitigated to increase the efficiency of autism care within the community. This research also points to the vitality of the community as a key component of the ECHO Ontario Autism program.
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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 | 0.059 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".