An Intervention to Improve Emergency Room Nurses' Swallow Study Implementation and Documentation
Bibliographic record
Abstract
Stroke is the 5th leading cause of death when isolated from other cardiovascular diseases in the United States (U.S.) (CDC, 2017). The Centers for Disease and Control (CDC) indicated that 795,000 people have a stroke each year with 65% suffering from some form of dysphagia, or difficulty swallowing. Implementing a simple screening by a trained nurse can detect dysphagia and prevent adverse outcomes, such as aspiration. In the emergency department (ED) of a suburban acute care facility, a pattern of inconsistent Toronto Bedside Swallow Screening Tool (TORBSST) was noted for 24 consecutive months. The purpose of this quality improvement (QI) project was to improve nursing knowledge, implementation, and documentation of the TORBSST prior to by mouth (PO) medication, food, and/or fluid administration. Three 15-minute education sessions followed by daily practice reminders in relation to TORBSST implementation and documentation through shift huddles and flyers were implemented. Baseline nursing knowledge was evaluated with a self-designed assessment administered before and two-months post presentation. No changes in mean knowledge scores were identified. Patient care outcomes data were obtained from the electronic health record for 2-months before and following the intervention. A 100% pre- and post-test accuracy response indicates that knowledge did not impact TORBSST implementation and documentation. Initiation of intentional reminders in conjunction with continual rewarding reinforced expected outcomes and improved TORBSST implementation and documentation. Future implications should include expanded observation and follow up time frames at multiple sites with a larger sample size. Annual nursing education in regard to TORBSST implementation and documentation policy and procedure should be considered for establishment and evaluation.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".