Bibliographic record
Abstract
Moving the culture of healthcare to embrace healing in addition to curing is a daunting task. Multiple approaches are required. One such approach has been the introduction of quarterly Whole Person Care (WPC) Rounds at our medical center. The Rounds are creatively developed by an interdisciplinary committee and are open to the entire hospital community, including non-clinicians, volunteers and hospice/home health staff. The Rounds have taken on a variety of formats, from panels of providers, panels of patients/family members, mixed panels and interactive workshops. Examples of titles are Mindfulness in the Workplace, Moral Dilemmas in Medicine, Flourishing in the Workplace, Whose Pain Is It, Anyway?, Death Over Dinner (at Lunch), Joy to the (Our) World. The format is a lunch meeting lasting one hour. The emphasis is always on some aspect of healing for patients, families, clinicians, non-clinical staff or volunteers. Mindfulness work is part of every Rounds. The Congress workshop format would allow an introduction of the concept, examples of previous Rounds, video clips of Rounds, feedback from attendees and interactive sample exercises performed with Congress learners accompanied by robust discussion.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".