Lessons from ‘ADHD Curriculum’ in Canada from the ‘Two Perspectives’ of Holistic Education
Bibliographic record
Abstract
이 연구의 목적은 캐나다 ‘ADHD 교육과정’을 분석하여 국내 ADHD 교육정책의 발전방향을 제언 하고, 이 분야의 ‘홀리스틱 교육’ 필요성을 제시하는 것이다. 이를 위해 캐나다 ‘ADHD 교육과정’을 홀리스틱 교육의 ‘두 관점(거시적·미시적)’으로 구분해 주제를 분석했다. 첫째, ‘거시적 주제’는 캐나다 정부의 ADHD 정책, ‘민간 사회단체(CADDAC과 CHAAD)’의 역할, 각 주 중심의 교육행정으로 분류했다. 둘째, ‘미시적 주제’는 ‘정신건강 교육(MHE)’ 기반의 생활지도, ‘보편적 학습설계(UDL)’, ‘개별화 교육계획(IEP)’, ‘배려적 수업’으로 분류했다. 이러한 주제 분석을 통해 본론에서 캐나다 ‘ADHD 교육과정’의 교훈을 종합하고, 제언에서 국내 ADHD 교육정책의 발전방향을 제시하고, 결론에서 ‘ADHD 학생교육’은 ‘홀리스틱 교육’의 관점과 실천이 필요함을 제시할 것이다.The purpose of this study is to analyze the ‘ADHD curriculum’ in Canada, propose the direction of development of ADHD education policy in Korea, and suggest the necessity of ‘Holistic education’ in this field. To do this, we analyzed the theme of ‘ADHD Curriculum’ in Canada by dividing it into ‘Two perspectives (Macroscopic and Microscopic)’ of ‘Holistic education’. First, ‘Macro topics’ were categorized as Canadian government’s ADHD policy, the role of ‘Private social organizations (CADDAC and CHAAD)’, and State - centered education administration. Second, ‘Micro topics’ were categorized as ‘Mental health education (MHE)’ based life guidance, ‘Universal learning design (UDL)’, ‘Individualized education plan (IEP)’, and ‘Caring lessen(or lecture)’. From this analysis, we can draw the lessons from ‘ADHD Curriculum’ in Canada and propose the direction of development of domestic ADHD education policy. In conclusion, ‘ADHD student education’ needs perspective and practice of ‘Holistic education’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".