MétaCan
Menu
Back to cohort
Record W4389496854 · doi:10.1097/nur.0000000000000792

Building Clinical Leadership Competencies When Caring for Hospitalized Adults Experiencing Dementia

2023· article· en· W4389496854 on OpenAlexaff
Patricia B. Bilski, Shawna V. Hudson, Margaret MacLellan

Bibliographic record

VenueClinical Nurse Specialist · 2023
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsGreenfield Research (Canada)Parks Canada
Fundersnot available
KeywordsDementiaMentorshipNursingMedicinePsychological interventionIntervention (counseling)PopulationPsychologyDiseaseMedical education

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.392
GPT teacher head0.513
Teacher spread0.121 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueClinical Nurse SpecialistSame topicPalliative Care and End-of-Life IssuesFrench-language works237,207