Using SBAR to communicate falls risk and management in inter-professional rehabilitation teams.
Bibliographic record
Abstract
This study implemented and evaluated the adapted Situation-Background-Assessment-Recommendation (SBAR) tool for use on two inter-professional rehabilitation teams for the specific priority issue of falls prevention and management. SBAR has been widely studied in the literature, but rarely in the context of rehabilitation and beyond nurse-physician communication. In phase one, the adapted SBAR tool was implemented on two teams with a high falls incidence over a six-month period. In phase two, process and outcome evaluations were conducted in a pre-post design comparing the impact of the intervention with changes in the rest of the hospital, including the perceptions of safety culture (as measured by the Hospital Survey on Patient Safety Culture); effective team processes, using the Team Orientation Scale; and safety reporting, including falls incidence, severity and near misses. This study suggests that the adapted SBAR tool was widely and effectively used by inter-professional rehabilitation teams as part of a broader program of safety activities. Near-miss and severity of falls incidence trended downward but were inconclusive, likely due to a short time frame as well as the nature of rehabilitation, which pushes patients to the limit of their abilities. While SBAR was used in the context of falls prevention and management, it was also utilized it in a variety of other clinical and non-clinical situations such as transitions in care, as a debriefing tool and for conflict resolution. Staff found the tool useful in helping to communicate relevant and succinct information, and to "close the loop" by providing recommendations and accountabilities for action. Suggestions are provided to other organizations considering adopting the SBAR tool within their clinical settings, including the use of an implementation tool kit and video simulation for enhanced uptake.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".