125: Factors Influencing ASD Screening by Community Paediatricians
Bibliographic record
Abstract
The prevalence of autism spectrum disorders (ASD) is estimated at 62 per 10 000 children based on systematic review of epidemiological surveys worldwide. In many cases, ASDs can be accurately diagnosed in children two to three years old and high risk children can be identified earlier than 24 months, but Canadian data shows a median age at diagnosis of 39 to 55 months. Despite advances in diagnosis of ASD, data on general paediatric practice regarding ASD screening in Canada is lacking. The objectives of this study were to examine general paediatricians' current practices regarding ASD screening and identify the factors that influence paediatricians' decisions for including ASD screening tools in their clinical practice. The aim was to obtain broad opinions and perspectives to support future research. Twelve paediatricians from four practice groups participated in four focus groups and one interview. Each focus group took 40 min to 60 min and was conducted using a semi-structured interview with targeted open ended questions, digitally recorded, and transcribed verbatim. A qualitative interpretive description approach was chosen since the aim was to present the participants' point of view by staying close to the data. All meaningful texts from the transcripts were coded, categorized and reviewed with the co-investigators and final themes were obtained with care taken to describe the participants' experiences in their own language. Five main categories were identified: 1) benefits, 2) limitations, 3) elements that limit utility, 4) elements that foster utility, and 5) process of application in ASD screening. Participants identified factors that influence their current practice such as availability of time, comfort and experience, and knowledge of specific tools. They also identified important barriers and facilitators to ASD screening including screening tool characteristics that influence their decisions to use a formal tool. Furthermore, the participants commented on the current infrastructure for ASD screening and possible changes, such as the participation of allied health professionals and implementation of guidelines that could affect accessibility for both diagnosis and ASD resources. This study identified factors that play into a paediatrician's role in formal ASD screening as well as obstacles and facilitators to implementing an ASD screening program. While the feasibility and effectiveness of a nationwide autism screening program remains controversial, this study provides grounds for future research questions with implications on policy and practice.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".