Introduction of GRACE program required for COVID-19 disaster - especially for exhausted healthcare workers
Bibliographic record
Abstract
The global pandemic of COVID-19 has exhausted many citizens around the world, especially health care workers. In times like these, mindfulness and compassion are necessary to heal many people. GRACE is a program that healthcare professionals can easily use in clinical practice. This program was developed by Roshi Joan Halifax, Ph.D., a Buddhist teacher, Zen priest, anthropologist, Cynda Rushton, the Bunting Chair of Ethics at Johns Hopkins University, and Professor Tony Back, a palliative care physician and medical oncologist at the University of Washington to prevent burnout among medical professionals. G.R.A.C.E. was developed in response to the deficit of compassion in the world today. The G.R.A.C.E. process includes the following five steps. G: Gathering attention and Grounding, R: Recalling intention, A: Attuning to self and other, C: Considering what will serve, E: Engaging and ending. G.R.A.C.E. is a tool for anyone, especially those in leadership roles or helping professions, such as medical workers, teachers, human rights workers, and more. In Japan, we invited three of the developers to hold training sessions since 2015. We held an annual conference every year, and monthly study sessions, in Tokyo and Osaka. Currently, we are planning to translate the online course developed in the US into Japanese and use it for training. In this presentation, we will report on the spread of G.R.A.C.E. in Japan, along with an overview of G.R.A.C.E.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.082 | 0.027 |
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".