Bibliographic record
Abstract
Objective: The purpose of this study was to analyze the major research fields and trends of autism spectrum disorder by analyzing domestic academic journals and dissertations published from 2010 to 2020 and mediated by occupational therapists. Methods: To analyze the level of research, intervention methods, dependent variables, and analysis tools, a total of 22 papers from January 2010 to April 2020 were selected through the National Assembly Library and the RISS. The key search terms were ‘자폐’, ‘작업치료’, and ‘감각통합’. Results: Among the studies released over the past 10 years, a ‘Single subject research design’ was the largest at 54.5%, followed by ‘One group non-randomized study’, ‘Two groups non-randomized study’, and a ‘Randomized Control Trial (RCT)’. The most frequently used independent variable was ‘Sensory integration therapy’, of which 14 out of 22 studies used sensory integration therapy as an intervention for Autism Spectrum Disorders (ASD), and the dependent variables were ‘Behavior level’ and ‘Occupational performance’, followed by ‘Sensory integration function’, ‘Cognition & perception’, and ‘Motor function’. A Sensory Profile (SP) was the most commonly used measurement tool, followed by the ‘Canadian Occupational Performance Measure (COPM)’ and ‘Video recording’. Conclusion: Through this literature review, we were able to find and understand the occupational therapist research trend for ASD. In addition, this study can be used as basic data to plan the research and education direction of Korean occupational therapy for autism spectrum disorders.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".