Community General Pediatricians' Perspectives on Providing Autism Diagnoses in Ontario, Canada: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: Community general pediatricians (CGPs) are a potential resource to increase capacity for autism spectrum disorder (ASD) diagnostic assessments. The objective of this study was to explore factors influencing CGPs' perspectives on and practices of providing ASD diagnoses. METHODS: This qualitative study used a constructivist modified grounded theory approach. Participants included CGPs who had attended ASD educational events or had referred a child with suspected ASD to a tertiary rehabilitation center. Individual in-depth interviews with CGPs were recorded, transcribed, and coded. An explanatory framework was developed from the data. A summary of the framework was sent to participants, and responses indicated that no changes were needed. RESULTS: Eleven CGPs participated. Assessment for ASD consists of 3 stages: (1) determining the diagnosis; (2) communicating the diagnosis; and (3) managing next steps after diagnosis. Each of these stages of ASD diagnostic assessment exists within an ecological context of child/family factors, personal CGP factors, and contextual/systems factors that all influence diagnostic decision making. CONCLUSION: Community general pediatrician ASD diagnostic capacity must be considered within the larger context of ASD care. Suggestions to improve diagnostic capacity include preparing families for the diagnosis, changing CGP perceptions of ASD, providing community-based training, improving financial remuneration, and providing service navigation. Further study is needed to ensure that CGPs are providing accurate, high-quality assessments.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".