PERSPECTIVES OF NON-URBAN GENERAL PEDIATRICIANS ON DIAGNOSING AUTISM SPECTRUM DISORDER
Bibliographic record
Abstract
Abstract BACKGROUND Wait times for autism spectrum disorder (ASD) diagnosis contribute to delayed access to ASD interventions, which are most effective at young ages. General paediatricians (GPs) represent a potential resource for increasing diagnostic capacity and decreasing wait times for ASD diagnostic assessments. No current research has yet to solicit perspectives of non-urban GPs on ASD diagnosis. OBJECTIVES The objective of this project was to explore the perspectives of GPs practicing in smaller Ontario communities regarding their diagnostic processes and their role in ASD diagnostic assessment. DESIGN/METHODS We performed a qualitative study using thematic analysis of GPs’ experiences with cases of suspected ASD. GPs from three small/medium-sized Ontario communities (population range: 14,000–64,000) were recruited to provide variation in perspectives. Semi-structured interviews were conducted, transcribed, and coded. A preliminary theoretical framework was developed and sent to participants with a request for feedback (member checking). Feedback did not necessitate any significant changes to the framework. RESULTS 14 GPs participated in interviews (7 from Community A, 4 from Community B, 3 from Community C). All participants considered ASD diagnosis to be in the GP’s scope of practice and part of their service to their community. Many sought out and self-funded additional ASD training as necessitated by their practices. Our framework identified three stages in the diagnosis of ASD: pre-assessment (gathering information prior to the first clinic visit), diagnosis, and service access. All of these stages are influenced by ecological factors including the individual patient/family, individual physician factors, factors reflecting the GP’s community group practice (i.e., how the group has decided its members will approach developmental referrals), and factors concerning the broader system of ASD care. CONCLUSION GPs practicing in non-urban Ontario communities routinely diagnose ASD and see this as part of their scope of practice. Compared to their urban counterparts, they emphasized a stronger sense of responsibility to provide this service to their communities. Strategies aimed at increasing diagnostic capacity should target group practices, increase availability of ASD specific training programs, and improve efficiency of clinic visits through pre-clinic information gathering. Further research regarding accuracy of GP assessments is required.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".