41 Content Analysis of the Recommendations from Project Echo Ontario Autism
Bibliographic record
Abstract
Abstract Background The rising prevalence of autism spectrum disorder (ASD) diagnoses has caused an increased number of community practitioners to care for this population. However, community practitioners report a lack of knowledge and confidence in treating these patients, resulting in unmet healthcare needs. The Extension of Community Healthcare Outcomes (ECHO) Autism model aims to address this through case-based and didactic learning to help guide community practitioners in providing comprehensive, best-practice care for ASD screening, diagnosis, and management of co-occurring conditions. Each ECHO session involves a case presentation followed by a list of recommendations generated by community participants and an interdisciplinary ‘hub’ team. While ECHO Autism has been shown to improve physicians' abilities to care for children with ASD in their practices, recommendations stemming from ECHO cases have yet to be characterized and may help guide future care. Objectives To quantify and characterize the common categories within ECHO Autism Ontario case recommendations. Design/Methods A content analysis of 422 recommendations from 61 ECHO cases was conducted to identify categories of recommendations and their frequencies. Three researchers independently coded recommendations from five ECHO cases, from which an original coding guide was developed. The researchers then independently coded the remaining cases and met regularly with the ECHO lead to modify and consolidate the codes and coding guide. From there, categories and sub-categories from the various codes were identified. Finally, the frequencies of each code and category were calculated. Results Fifty-seven codes were included in the final coding guide and grouped into eight broad categories. Categories included: 1) diagnosis; 2) concurrent mental and physical health conditions; 3) referrals to allied health providers and other specialists; 4) accessing community resources, such as parent and sibling support groups; 5) providing education and guidance to physicians, patients, and families; 6) management strategies such as nutrition, physical activity, and social skills; and 7) patient and family-centered care. A COVID-19 category was added, as many of the later recommendations were adapted to online service delivery. An analysis of the frequency of codes found that 1,384 total in-text codes were distributed amongst the various categories. The three highest frequencies of categories were providing general guidance and education (22%), accessing resources (16%), and referrals (15%). Conclusion This is the first time recommendations from ECHO Autism have been characterized and quantified. Our results, particularly the most common category of providing general guidance and education about ASD, show there is still important work to be done with educating clinicians and families about aspects of ASD. Furthermore, findings from this study should inform Pediatrics residency programs about real-world knowledge gaps in ASD care, and may help create more tailored ASD training programs and educational materials.
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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 | 0.026 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".