Transforming Challenging Patients into Interesting People - Creative Writing as Burnout Prevention for Health Professionals
Bibliographic record
Abstract
And now for something completely different….. As health practitioners, we are involved in writing “stories” every day. The patient record is our interpretation of our patients’ stories (History) and a summary of our response to this (Examination and Management Plan).This record does not allow for much creativity on the part of the writer, and is very limited in its ability to assist the health practitioner in making sense of what has gone on for them at a personal level. To assist in remedying this problem, this workshop will use creative writing as a tool to assist in burnout prevention.The workshop will allow participants an opportunity to experience the use of stories and creative writing as a means of helping them to better manage some of the more challenging aspects of their working life, and to better make sense of what it means to be a health practitioner.Practical writing exercises across a range of styles will assist participants in reflecting on the effect their clinical practice has on their lives with the goal of increasing their enjoyment of work, and of life in general. These simple writing exercises will magically transform challenging patients into interesting people. No previous writing experience is required. Most of all, it will be an opportunity for some light hearted fun with colleagues.Learning Objectives: By the end of the workshop, participants will have had the opportunity to1. Learn knew skills in creative writing2. Reflect on what it means to be a health practitioner through the use of structured writing exercises3. Develop skills in using reflective writing as a way of personal debriefing about experiences at work4. Reignite previously lost passions for creativity5. Marvel at the brilliance of their colleagues6. Share some of their creative brilliance with colleagues, if they choose to.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".