Building Clinical Leadership Competencies When Caring for Hospitalized Adults Experiencing Dementia
Bibliographic record
Abstract
PURPOSE AND OBJECTIVES: Attempting to improve the experience of hospitalized adults with dementia and reduce patient attendant costs, we addressed hospital nursing staff confidence managing responsive behaviors through education, mentorship, and individualized patient care planning for adults with dementia.Responsive behaviors (such as pacing, calling out) is a term used to describe behaviors demonstrated by a person with dementia as a way of responding to something negative, frustrating, or confusing in their social and physical environment. DESCRIPTION OF PROJECT: Under time restraints, we performed a rapid environmental scan and developed internal clinical resources and a learning strategy that informed a quality improvement initiative that focused on dementia care of hospitalized patients. OUTCOME: Using quantitative and qualitative evaluation methods, the interventions increased confidence, competency, and leadership in clinical nursing leaders and improved person-centered care planning practices. The cost of patient attendant usage for this patient population decreased by 28% in 1 year. CONCLUSION: This intervention, which was not a copyrighted program associated with administration costs, improved hospital-based dementia care and staff confidence in dementia care and reduced annual costs associated with patient attendant useage.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".