Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.201 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.096 |
| Scholarly communication | 0.022 | 0.037 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".