Bibliographic record
Abstract
Amidst the commotion of constant changes in health care systems, budget cuts, burnout and compassion fatigue there are resilient clinicians who relieve suffering and promote healing in those who seek their care. This workshop will focus on how doctors, nurses, and allied health care professionals serve in this way while maintaining equanimity and sense of meaning in their work and personal lives.This 90-minute experiential-based workshop will be divided into three parts.First, Mindful Clinical Practice will be described using narratives from different health care professionals in various settings. Mindful Congruence will be defined, along with Satir’s four other communication stances.Second, how the Four Noble Truths stemming from Buddhist philosophy inform clinical practice will be discussed with an emphasis on the Eightfold Path to end suffering. Third, a model of Healing Relationships (Scott et al, 2008; 2009) will be used to help participants identify underlying processes contributing to the relational outcomes: hope, trust, and being known. An Appreciative Inquiry exercise will be used to enrich participants’ understanding of their own experiences of being healers in clinical encounters.If and how medicine may be a spiritual practice will be examined.At the end of the workshop participants will be able to: 1. Define Mindful Congruence.2. Understand how the Four Noble Truths from Buddhist philosophy inform clinical practice.3. See how meditation practice contributes to clinicians’ mindfulness and emotional regulation.4. Discern the competencies and processes underlying healing.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.054 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".