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Record W2070589796 · doi:10.1111/jan.12501

A case for the use of autoethnography in nursing research

2014· article· en· W2070589796 on OpenAlexaff
Ashley L. Peterson

Bibliographic record

VenueJournal of Advanced Nursing · 2014
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of ManitobaVancouver Coastal Health
Fundersnot available
KeywordsAutoethnographyCINAHLContext (archaeology)PsycINFONarrativeNarrative inquiryQualitative researchNursingPsychologySociologyMEDLINEMedicineSocial sciencePsychological interventionArt

Abstract

fetched live from OpenAlex

AIMS: This paper discusses the basis for and potential usefulness of autoethnography as a research method in nursing. BACKGROUND: While qualitative research in nursing has traditionally involved the researcher taking an objective stance, autoethnography, with roots in the social sciences, is an emerging method that examines the researcher's own experience in a cultural context. DESIGN: Discussion paper. DATA SOURCES: Data sources from 1979-2013 in the CINAHL, Medline and PsycInfo databases were drawn on including articles from nursing and social science journals on autoethnography and related narrative-based approaches. DISCUSSION: Autoethnography is based on the assumption that reality is multifaceted and the role of culture and context is crucial in understanding human experience. The reader is engaged through the evocation of emotion and the stimulation of reflection. IMPLICATIONS FOR NURSING: While autoethnography has thus far been little used in the discipline of nursing, it is a methodology that offers novel insights and an opportunity to examine the impact of nurses' personal and professional cultural identity on their practice. CONCLUSION: Through the use of a subjective lens, autoethnography gives nurses the opportunity to tell stories that would otherwise not be heard. It involves a courageous laying bare of the self to gain new cultural understandings and it offers the potential for nurses to learn from the experiences and reflections of other nurses.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.115
GPT teacher head0.448
Teacher spread0.334 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations42
Published2014
Admission routes1
Has abstractyes

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