Factors influencing autism spectrum disorder screening by community paediatricians
Bibliographic record
Abstract
BACKGROUND: In most cases, autism spectrum disorders (ASD) can be reliably diagnosed at two to three years of age. However, Canadian data reveal a median age at diagnosis of approximately four years. OBJECTIVE: To examine general paediatricians' practices regarding ASD screening and identify factors that influence decisions regarding the use of ASD screening tools. METHODS: Using a qualitative inquiry-based interpretive description approach, 12 paediatricians from four practice groups participated in four focus groups and one individual interview. These were conducted using semistructured interviews, digitally recorded and transcribed verbatim. RESULTS: Five main domains of themes were identified related to screening tool use: benefits; needs not addressed; elements that limit utility; elements that encourage utility; and implementation challenges. Factors influencing practice included availability of time, comfort with screening tool use, previous use and knowledge about specific tools. Systemic factors included knowledge and access to community resources, as well as the ability to provide support to the child and family. CONCLUSION: The results from the present study identified important factors that influence paediatric practice in ASD screening. As screening tools improve, it will be important to examine the implementation and effectiveness of screening tools and strategies for increased uptake. Future research will also need to attend to the practical needs of physicians and communities in the aim of earlier diagnosis and rapid access to interventional resources.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".