Bibliographic record
Abstract
To improve the health of people in Taiwan, we have to come up with a visionary health blueprint. The purpose of this paper is to learn the experiences from USA, Japan, Canada and the European Region about how to write a white paper on public healthy as references and guidelines for Taiwan. The study methods include literature review and participating conferences such as ”Healthcare 21 Seminar of USA and Japan” and ”Healthy 21st Century: A Seminar of Experience Sharing from Canada” to gather relevant information. The results show that: (1) Planning visionary health policies and strategies for the society is an important force to achieve health for all; (2) The most determinants of population health are supportive health-enhanced environments, healthy lifestyle, and quality healthcare services; (3) The government and the private sectors have to set priority based on scientific evidence to meet the increasing demand under limited resources; (4) During the determination of policies and strategies, it is easier to come to a consensus through various brainstorming and discussion processes; (5) It is very important to make parternership with local governments and private sectors and to develop cross-sectorial parternership within the government. In conclusion, to publish a white paper on the health of people is essential, Taiwan has to build our own blueprint of ”2020 healthy people” after learning the experiences from other countries.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".