The knowledge of physicians regarding autism spectrum disorder (ASD) across Ontario: a mixed methods study
Bibliographic record
Abstract
The current study is a manuscript-based thesis, divided into 2 journal articles. The first article examines the facilitators and barriers to recruiting physicians for psychological research and is a reflexive article written from the author’s personal perspective. Physicians have been a hard population to recruit for research purposes over the years and the article provides insight into recruitment methods from a unique perspective. The second article investigates the knowledge of Ontario physicians regarding autism spectrum disorder (ASD), employing a mixed methods approach. ASD is a complex disorder and is rising in prevalence. Physicians are said to be one of the first to come into contact with an individual with ASD and need to be able to recognize or identify signs and symptoms of the disorder. Previous research highlights that physicians may not feel competent in diagnosing and treating ASD because of their lack of knowledge and training. Physicians completed questionnaires and participated in semi-structured interviews. Analysis of quantitative data included t-tests and ANOVA’s, while thematic analysis was employed to analyze the interview transcripts. Areas where knowledge or information regarding ASD was lacking is discussed. Additionally, recommendations for improving medical education regarding ASD and physicians’ knowledge, as well as clinical and research implications are discussed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 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".