Building Capacity for Community Pediatric Autism Diagnosis: A Systemic Review of Physician Training Programs
Bibliographic record
Abstract
OBJECTIVES: Training primary care providers to provide diagnostic assessments for autism spectrum disorder (ASD) decreases wait times and improves diagnostic access. Outcomes related to the quality of these assessments and the impacts on system capacity have not been systematically examined. This systematic review identifies and summarizes published studies that included ASD diagnostic training for primary care providers (PCPs) and aims to guide future training and evaluation methods. METHODS: Systematic searches of electronic databases, reference lists, and journals identified 6 studies that met 3 inclusion criteria: training for PCPs, community setting, and training outcome(s) reported. These studies were critically reviewed to characterize (1) study design, (2) training model, and (3) outcomes. RESULTS: All studies were either pre-post design or nonrandomized trials with a relatively small number of participants. There was considerable heterogeneity among studies regarding the training provided and the program evaluation process. The most evaluated outcomes were access to autism diagnosis and accuracy of diagnosis. CONCLUSION: Training PCPs to make ASD diagnoses can yield high diagnostic agreement with specialty teams' assessments and reduce diagnostic wait times. Current data are limited by small sample size, poor to fair quality study methodology, and heterogenous study designs and outcome evaluations. Evidence is insufficient to draw conclusions about the overall effects of training PCPs for ASD diagnostic assessments. Since further research is still needed, this review highlights which outcomes are relevant to consider when evaluating the quality of ASD assessments across the continuum of approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".