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Record W2917124544 · doi:10.1891/1078-4535.25.1.17

The LOTUS: A Journey to Value-Based, Patient-Centered Care

2019· article· en· W2917124544 on OpenAlexaff
Liza Barbarello Andrews, Nina Roberts, Carol Reed Ash, Natalie Jones, Meghan Rolston, Melina Hughes, Joanne Pelligrino, Ted Κ. Taylor

Bibliographic record

VenueCreative Nursing · 2019
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsMultidisciplinary approachHealth careScope (computer science)RestructuringSustainabilityNursingQuality (philosophy)Scope of practiceTeamworkPsychologyLotusProcess managementMedicineSociologyBusinessManagementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

In response to the merger of our 248-bed community hospital with a new health system, a multidisciplinary team began a journey of holistic transformation via the evolution of a new rounding process called Leadership, Ownership, Transformation, Unity, and Sustainability (LOTUS) in the 20-bed ICU. Morphing from a hierarchical practice structure with limited engagement of multidisciplinary members, the LOTUS initiative (named for the blossom whose petals surround its core, the patient) afforded each discipline (petal) an equal voice and allowed a once-fragmented team to work cohesively, collaboratively, and at the highest level of the scope of practice for each discipline, thus affording expert guidance during care planning while providing a method to collect quality metrics. LOTUS allows us to view our patients in a new way as we refocused goal determination on patients and their families. The restructuring and evolution into a high-functioning team was targeted with the goal of enhancing quality critical care for patients, which, in the literature, has correlated with improved patient safety and decreased mortality and ICU length of stay.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0090.009
Scholarly communication0.0150.009
Open science0.0020.020
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0080.002

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 head0.088
GPT teacher head0.411
Teacher spread0.323 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2019
Admission routes1
Has abstractyes

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