Bibliographic record
Abstract
Ⅰ. 서 론 1. 연구의 필요성 2. 연구목적 3. 연구방법 Ⅱ. 국외 인터넷 건강정보 서비스 동향분석 1. NHS Direct Online 2. MedlinePlus 3. HealthFinder 4. Canadian Health Network 5. HealthInsite 6. Better Health Channel 7. HON 8. CISMeF 9. 요약 및 시사점 Ⅲ. 인터넷 서비스 동향분석 1. 차세대 웹, 웹 2.0(Web 2.0) 2. 지식검색서비스 3. e헬스 윤리코드 Ⅳ. 「건강정보광장」운영현황 및 분석 1. 콘텐츠 구성 및 기능 2. ‘건강정보광장’의 자원 현황 3. 요약 및 향후 발전방향 Ⅴ. 「건강정보광장」 이용실태 분석 1. 이용자 만족도 분석 및 수요조사 2. 정량적 분석(웹트렌드+모니터링) 3. 요약 및 시사점 Ⅵ. 추가개발 및 시스템 구성 1. 시스템 보완 및 추가개발 2. 시스템 구성 Ⅶ. 인터넷 건강정보 질 관리체계 1. 인터넷 건강정보 출판표준 2. 인터넷 건강정보 콘텐츠 평가 틀 및 평가표 Ⅷ. 「건강정보광장」 발전방안 1. 다양한 저작요소와 사용자 편리성을 고려한 홈페이지 구성이 필요 2. 장애관리 및 개인정보보호를 위한 방안마련 3. 건강정보 홈페이지의 홍보방안이 마련 4. 국가건강지식정보의 연계 방안 마련 5. ‘건강정보광장’ 이용촉진 방안 마련 6. 건강지식 및 정보의 품질유지 방안
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.157 | 0.101 |
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".