Implementation of the Richmond Agitation-Sedation Scale (palliative version) on an inpatient palliative care unit
Bibliographic record
Abstract
BACKGROUND: The Richmond Agitation-Sedation Scale - Palliative version (RASS-PAL) tool is a brief observational tool to quantify a patient's level of agitation or sedation. The objective of this study was to implement the RASS-PAL tool on an inpatient palliative care unit and evaluate the implementation process. METHODS: Quality improvement implementation project using a short online RASS-PAL self-learning module and point-of-care tool. Participants were staff working on a 31-bed inpatient palliative care unit who completed the RASS-PAL self-learning module and online evaluation survey. RESULTS: The self-learning module was completed by 49/50 (98%) of regular palliative care unit staff (nurses, physicians, allied health, and other palliative care unit staff). The completion rate of the self-learning module by both regular and casual palliative care unit staff was 63/77 (82%). The follow-up online evaluation survey was completed by 23/50 (46%) of respondents who regularly worked on the palliative care unit. Respondents agreed (14/26; 54%) or strongly agreed (10/26; 38%) that the self-learning module was implemented successfully, with 100% agreement that it was effective for their educational needs. CONCLUSION: Using an online self-learning module is an effective method to engage and educate interprofessional staff on the RASS-PAL tool as part of an implementation strategy.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".