An Innovative Self-Care Module for Palliative Care Medical Learners
Bibliographic record
Abstract
Palliative care is a uniquely demanding field in that clinicians routinely address the complex needs of patients living with incurable illness. Due to their relative inexperience, medical learners completing a palliative care educational experience are particularly vulnerable to the stresses that are often encountered. To address this educational need, a structured Self-Care Module was developed for medical learners rotating through a palliative care clinical rotation. Components of this module include completion of a process recording exercise, a structured reflection, and participation in a facilitated group discussion. An examination of the acceptability, utility, and operational feasibility of the module demonstrated that 86% (n=35) of learners found the module helpful in reflecting on their clinical encounters, 86% (n=35) gained an appreciation for the importance of self-reflection and self-awareness as a component of self-care and 97% (n=35) gained a greater appreciation for sharing clinical experiences with other learners. This novel Self-Care Module was found to be a well accepted, useful, and operationally feasible educational experience for postgraduate and undergraduate learners completing a palliative care educational experience.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".