MétaCan
Menu
Back to cohort
Record W2749033346 · doi:10.1177/1054773817724961

Professional Socialization: A Grounded Theory of the Clinical Reasoning Processes That RNs and LPNs Use to Recognize Delirium

2017· article· en· W2749033346 on OpenAlex
Mohamed El Hussein, Sandra P. Hirst, Joseph Osuji

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Nursing Research · 2017
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsUniversity of CalgaryMount Royal University
Fundersnot available
KeywordsSocializationDeliriumGrounded theoryCognitionPsychologyCoding (social sciences)Theoretical samplingSocial psychologyQualitative researchPsychiatrySociology

Abstract

fetched live from OpenAlex

Delirium is an acute disorder of attention and cognition. It affects half of older adults in acute care settings and is a cause of increasing mortality and costs. Registered nurses (RNs) and licensed practical nurses (LPNs) frequently fail to recognize delirium. The goals of this research were to identify the reasoning processes that RNs and LPNs use to recognize delirium, to compare their reasoning processes, and to generate a theory that explains their clinical reasoning processes. Theoretical sampling was employed to elicit data from 28 participants using grounded theory methodology. Theoretical coding culminated in the emergence of Professional Socialization as the substantive theory. Professional Socialization emerged from participants' responses and was based on two social processes, specifically reasoning to uncover and reasoning to report. Professional Socialization makes explicit the similarities and variations in the clinical reasoning processes between RNs and LPNs and highlights their main concerns when interacting with delirious patients.

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.

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)0.007
metaresearch head score (Gemma)0.424
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.424
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
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 head0.370
GPT teacher head0.571
Teacher spread0.201 · 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