Implementation of an educational intervention pilot for residents on acute care general internal medicine wards around the ‘comfort measures strategy’ for end of life care
Bibliographic record
Abstract
Purpose This multi-component educational intervention was aimed at General Internal Medicine residents’ perceived self-efficacy in providing end of life care. This study also measured the uptake of the Comfort Measures Order Set. Methods This non-randomized study was conducted over nine 4-week rotations on one General Internal Medicine ward. The intervention consisted of: 1) a didactic module, 2) presence of the Palliative Care Consult Team at General Internal Medicine rounds and, 3) provision of end of life care educational materials. Twenty learners completed a pre/post Self-Efficacy in Palliative Care Scale. Data/Results Data revealed improved self-efficacy ratings on the overall scale, and on all three subscales of the Self-Efficacy in Palliative Care Scale. The Comfort Measures Order Set was implemented in 62% of patient deaths in the intervention group, and 51% of patient deaths in the control group, demonstrating no statistical difference between these groups. Conclusion The uptake of the order set in both the intervention and control groups demonstrated utility in providing a clinical framework for delivering end of life care and highlighted the need for on-going education and enhancement of clinicians' self-efficacy in end of life 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".