Bibliographic record
Abstract
隨著精準醫學與基因科技的快速發展，遺傳諮詢已成為當代健康照護體系必然的一環，本文運用「藍海策略」的核心概念，說明社會工作專業在此新興場域中所面臨的挑戰與發展契機；相較於歐美國家，亞洲地區遺傳諮詢專業的制度化與專業分工仍處於發展階段，社會工作在其中的定位尤具高度脈絡性與文化特殊性。本文以Laurino等人(2018)提出之亞太地區十國遺傳諮詢專業現況報告為基礎，聚焦香港與台灣兩地，從「社會工作教育」與「遺傳諮詢與社會工作實務」兩個面向進行討論，社會工作者在遺傳諮詢多專業團隊中，具備有效連結醫療體系與家庭脈絡、整合心理社會資源及回應文化與倫理議題的關鍵功能。未來，透過強化相關教育訓練與明確建構專業角色，社會工作可在基因醫療與精準健康照護中發揮其重要且獨特的專業價值。
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".