Ontario physicians' perceived competency when providing care for individuals with ASD
Bibliographic record
Abstract
The current document is a manuscript-based thesis investigating the overall perceived \nknowledge, competency and experiences of Ontario physicians when diagnosing and treating \nindividuals with Autism Spectrum Disorder (ASD). A growing body of literature has revealed \nthat physicians often do not feel comfortable providing care for patients with ASD due to lack of \neducation, training, exposure, and interest working with this population. However, there has been \na recent shift in the literature focusing on identifying factors that enhance the healthcare system \nfor patients with ASD as well as barriers that physicians encounter when diagnosing and treating \nthese individuals. Therefore, the first paper included in this thesis is a mixed-methods analysis of \nphysicians’ perceived knowledge and competency in terms of diagnosis and treatment of ASD. \nDespite their high perceived knowledge regarding the diagnosis and treatment of ASD, medical \npractitioners expressed their needs for further education and training regarding ASD. The second paper included is also a mixed-methods analysis examining factors that hinder and facilitate \nphysicians’ abilities to provide appropriate care for individuals with ASD. Descriptive statistics, \npaired-samples T-tests, repeated measures ANOVA, and chi-square analyses were used to \nanalyze the results of a questionnaire and thematic analysis was used to analyze the semistructured \ninterviews. Recommendations for improving the healthcare and educational systems \nas well as implications for enhancing physicians’ knowledge, competency and experiences are \ndiscussed.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 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) | 0.007 | 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".