Building resilience: an innovative reflective writing method for clinical palliative care – the 55 word story
Bibliographic record
Abstract
Finding innovative reflective self-care techniques reduces the potential for burnout and the stress associated with attending to the needs of the very ill and the dying. Time is often a barrier to self care; and narrative methodologies often seem to require too much time or writing ability. We offer a novel, time efficient, practical approach for debut at AAHPM/HPNA that is useful to almost everyone.The 55 word medical narrative about clinical encounters from the perspective of the clinician is the self care therapeutic tool offered during this session. Participants will experience and leave empowered to approach the medical narrative in a brief but meaningful way. In this workshop session, participants will be introduced to pertinent research and content on narrative medicine, and will participate in writing a 55 word story about a personal or professional encounter in hospice and palliative care, or about a topic that they want to explore in palliative care such as hope, compassion, doubt, or guilt.Participants will share their 55 word story in dyads, give feedback on this method and its impact on resilience and reflection.Objectives:1. Describe a novel, effective yet brief framework for the use of medical narrative as a reflective exercise for increasing resilience within the larger literature of narrative medicine methods.2. Demonstrate and experience the 55 word medical narrative as a brief but effective reflective exercise.3. Integrate the 55 word story narrative method into various clinical care and teaching settings in palliative care.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".