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Record W2739066951 · doi:10.5430/jnep.v7n12p28

Addressing the development of both knowledge and clinical reasoning in nursing through the perspective of script concordance: An integrative literature review

2017· article· en· W2739066951 on OpenAlexaffvenue
Marie‐France Deschênes, Johanne Goudreau

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsycINFOConcordancePerspective (graphical)CognitionPsychologySituatedScripting languageMEDLINEScopusSituational ethicsKnowledge managementNursingComputer scienceMedicineSocial psychologyArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

Background: In clinical practice settings, where situations of uncertainty exist, clinical reasoning is situated or contextualized. It calls upon honed knowledge, wherein nurses rely on highly-developed and organized knowledge networks known as “mental scripts”.Methods: The aim of this integrative literature review was to address ways to develop knowledge and clinical reasoning in nursing through the use of mental scripts, and to tackle these pedagogical considerations. The literature search was performed using the following data sources: CINHAL, MEDLINE Google Scholar, PubMed, ProQuest, PsycINFO, Scopus and Web of Science.Results: Script concordance, which optimises situated clinical reasoning, ties in with the socio-cognitive perspective of cognitive apprenticeship, using role models to guide the development of knowledge and clinical reasoning in nursing. Moreover, this perspective proposes implementing new teaching strategies, which focus on situational awareness, reflective acuity, and cognitive dialogue.Conclusions: The perspective of script concordance allows a foreseeable innovative formulation of practices favourable to the development of clinical reasoning in nursing.

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)0.003
metaresearch head score (Gemma)0.098
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.098
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.186
GPT teacher head0.576
Teacher spread0.389 · 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 designOther design
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

Citations23
Published2017
Admission routes2
Has abstractyes

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