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Clinical Nurses’ Understanding of Autonomy

2004· article· en· W2021888275 on OpenAlexaff
Janice Stewart, Katherine Stansfield, Dianne M. Tapp

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2004
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsAlberta Bone and Joint Health InstituteUniversity of Calgary
Fundersnot available
KeywordsAutonomyMultidisciplinary approachNursingPsychologyFocus groupMedicineMedical educationSociology

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of the study was to enable nurse managers to identify strategies to support and enhance autonomous practice based on clinical nurses' understanding of autonomy. BACKGROUND: Findings from an organizational work-life satisfaction survey led a nursing management team to question how clinical nurses understand autonomy. The nursing literature offers inconsistent definitions of autonomy and interchangeable use of related concepts. METHODS: Twelve focus groups involving 43 nurses working in cardiovascular service units discussed instances of satisfaction and dissatisfaction with autonomy in their clinical practice and work life. Verbatim transcripts of group discussions were interpreted by a research team to identify salient examples and descriptions of autonomy. RESULTS: Nurses described autonomy as their ability to accomplish patient care goals in a timely manner by using their knowledge and skills to understand and contribute to the overall plan of care; assess patient needs and conditions; effectively communicate concerns and priorities regarding patient care; and access and coordinate the resources of the multidisciplinary team. CONCLUSIONS: These findings challenge assumptions about autonomy as independent decision making and practice. They highlight nurses' contributions to patient care goals through knowledge of how to get things done within hospital systems and through interdisciplinary coordination and collaboration.

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)0.013
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.368
GPT teacher head0.602
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations64
Published2004
Admission routes1
Has abstractyes

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