Summative content analysis of the recommendations from Project ECHO Ontario Autism
Bibliographic record
Abstract
Background Practitioners report a lack of knowledge and confidence in treating autistic children, resulting in unmet healthcare needs. The Extension of Community Healthcare Outcomes (ECHO) Autism model addresses this through discussion of participant-generated cases, helping physicians provide best-practice care through co-created recommendations. Recommendations stemming from ECHO cases have yet to be characterized and may help guide the future care of autistic children. Our objective was to characterize and categorize case discussion recommendations from Project ECHO Ontario Autism to better identify gaps in clinician knowledge. Methods We conducted a summative content analysis of all ECHO Ontario Autism case recommendations to identify categories of recommendations and their frequencies. Two researchers independently coded recommendations from five ECHO cases to develop the coding guide. They then each independently coded all remaining cases and recommendations from three cycles of ECHO held between October 2018 to July 2021, meeting regularly with the ECHO lead to consolidate the codes. A recommendation could be identified with more than one code if it pertained to multiple aspects of autism care. Categories from the various codes were identified and the frequency of each code was calculated. Results Of the 422 recommendations stemming from 62 cases, we identified 55 codes across ten broad categories. Categories included accessing community resources (n = 224), referrals to allied health and other providers (n = 202), ongoing autism care (n = 169), co-occurring mental and physical health conditions (n = 168), resources and tools for further learning (n = 153), physician to provide education and coaching to families (n = 150), promoting parent and family wellness (n = 104), supporting community autism diagnosis (n = 97), promoting patient empowerment and autonomy (n = 87), and COVID-19 (n = 26). Conclusion This is the first time that recommendations from ECHO Autism have been characterized and grouped into categories. Our results show that advice for autism identification and management spans many different facets of community-based care. Specific attention should be paid to providing continued access to education about autism, streamlining referrals to allied health providers, and a greater focus on patient- and family-centered care. Physicians should have continued access to autism education to help fill knowledge gaps and to facilitate families' service navigation.
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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 | 0.053 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".