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Record W2737925552 · doi:10.1177/1074840717717731

Strengths-Based Nursing: A Process for Implementing a Philosophy Into Practice

2017· article· en· W2737925552 on OpenAlexaff
Laurie N. Gottlieb, Bruce Gottlieb

Bibliographic record

VenueJournal of Family Nursing · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsContext (archaeology)Process (computing)NursingEmpowermentValue (mathematics)CuriosityPsychologyPhase (matter)Health careKnowledge managementMedicinePublic relationsSocial psychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Strengths-Based Nursing (SBN) is both a philosophy and value-driven approach that can guide clinicians, educators, manager/leaders, and researchers. SBN is rooted in principles of person/family centered care, empowerment, relational care, and innate health and healing. SBN is family nursing yet not all family nursing models are strengths-based. The challenge is how to translate a philosophy to change practice. In this article, we describe a process of implementation that has organically evolved of a multi-layered and multi-pronged approach that involves patients and families, clinicians, educators, leaders, managers, and researchers as well as key stakeholders including union leaders, opinion leaders, and policy makers from both nursing and other disciplines. There are two phases to the implementation process, namely, Phase 1: pre-commitment/pre-adoption and Phase 2: adoption. Each phase consists of distinct steps with accompanying strategies. These phases occur both sequentially and concurrently. Facilitating factors that enable the implementation process include values which align, readiness to accept SBN, curiosity-courage-commitment on the part of early adopters, a critical mass of early adopters, and making SBN approach both relevant and context specific.

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)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.211
GPT teacher head0.533
Teacher spread0.322 · 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.

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

Citations73
Published2017
Admission routes1
Has abstractyes

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