Japan Medical Association Junior Doctors Network Report on the WMA General Assembly, Durban 2014.
Bibliographic record
Abstract
The Junior Doctors Network (JDN), approved by the World Medical Association (WMA) in 2010, is an international network of junior doctors. The Japan Medical Association Junior Doctors Network (JMA-JDN) was established as the national JDN in Japan in 2012 under the JMA Global Health Committee and started its activities as a member of the WMA-JDN in 2013. This is the report of the 3 JMA-JDN members who participated in the 2-day JDN Conference held on October 12 and 13 preceding the 2015 WMA General Assembly in Moscow. More than 30 junior doctors from 17 countries—Japan, South Korea, Myanmar, the Philippines, the United States, Canada, Peru, Brazil, Turkey, Greece, Portugal, Spain, Germany, Macedonia, Kuwait, Nigeria, and South Africa—gathered at this year’s JDN Conference. Three members from Japan—Abe, Mishima, and Kishi—attended the conference. The 2-day conference included activity reports by each country, proposals for policy statements, and a board member election, and we received encouragement and lectures from the WMA leaders.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.075 | 0.030 |
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".