Palliative Care SBAR - A story of forbidden love between SBAR and TWTW with some GRRRR throw in as well
Bibliographic record
Abstract
This presentation relates the tale of forbidden love that developed between the Situation - Background - Assessment - Recommendation (SBAR) tool and Te Whare Tapa Wha (TWTW) a New Zealand Maori model of health and wellness that has led to the creation of their love-child - the Palliative Care SBAR.Clinical situations can be thought of as stories that need to be shared between healthcare practitioners at relevant times. Communication of such stories can be difficult if the participants do not have appropriate tools available. The SBAR was originally developed by the United States Navy as a communication tool, but is now widely used in healthcare settings for clinical "hand-off"/hand-overs. TWTW is a New Zealand Maori model of health and wellness first developed by Maori health expert Professor Sir Mason Durie in 1982, and has become widely used by New Zealand Palliative Care teams as it provides a framework for holistic, whole person care provision. The Greet, Respectfully listen, Review, Recommend, Reward (GRRRR) listening model provides a formula for listeners to follow. Intrigued yet? Come along to the presentation to see how a budding raconteur, James Jap, cobbles these disparate story elements together. It will be a bit different. You have been warned…
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".