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Recognizing and Responding to Uncertainty: A Grounded Theory of Nurses’ Uncertainty

2012· article· en· W2073265108 on OpenAlexafffundabout
Lisa Cranley, Diane Doran, Ann E. Tourangeau, André Kushniruk, Lynn Nagle

Bibliographic record

VenueWorldviews on Evidence-Based Nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsSt. Lawrence CollegeUniversity of TorontoUniversity of VictoriaUniversity of AlbertaMinistry of Health and Long Term Care
FundersCanadian Institutes of Health Research
KeywordsGrounded theoryPsychologyUncertainty theoryEpistemologySociologySocial psychologyMathematicsQualitative researchStatisticsSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

BACKGROUND: There has been little research to date exploring nurses' uncertainty in their practice. Understanding nurses' uncertainty is important because it has potential implications for how care is delivered. PURPOSE: The purpose of this study is to develop a substantive theory to explain how staff nurses experience and respond to uncertainty in their practice. METHODS: Between 2006 and 2008, a grounded theory study was conducted that included in-depth semi-structured interviews. Fourteen staff nurses working in adult medical-surgical intensive care units at two teaching hospitals in Ontario, Canada, participated in the study. FINDINGS: The theory recognizing and responding to uncertainty characterizes the processes through which nurses' uncertainty manifested and how it was managed. Recognizing uncertainty involved the processes of assessing, reflecting, questioning, and/or being unable to predict aspects of the patient situation. Nurses' responses to uncertainty highlighted the cognitive-affective strategies used to manage uncertainty. DISCUSSION: Study findings highlight the importance of acknowledging uncertainty and having collegial support to manage uncertainty. The theory adds to our understanding the processes involved in recognizing uncertainty, strategies and outcomes of managing uncertainty, and influencing factors. IMPLICATIONS: Tailored nursing education programs should be developed to assist nurses in developing skills in articulating and managing their uncertainty. Further research is needed to extend, test and refine the theory of recognizing and responding to uncertainty to develop strategies for managing uncertainty. CONCLUSIONS: This theory advances the nursing perspective of uncertainty in clinical practice. The theory is relevant to nurses who are faced with uncertainty and complex clinical decisions, to managers who support nurses in their clinical decision-making, and to researchers who investigate ways to improve decision-making and care delivery.

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)0.037
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0060.034
Scholarly communication0.0100.011
Open science0.0040.008
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.396
Teacher spread0.305 · 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 designQualitative
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

Citations46
Published2012
Admission routes3
Has abstractyes

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