A Critical Comparison of Focused Ethnography and Interpretive Phenomenology in Nursing Research
Bibliographic record
Abstract
Choosing an appropriate qualitative methodology in nursing research is a researcher's first step before beginning a study. Such a step is critical as the selected qualitative methodology should be congruent with the research questions, study assumptions, data gathering and analysis to promote the utility of such research in enhancing nursing knowledge. In this paper, we compare focused ethnography by Roper and Shapira and interpretive phenomenology by Benner. Though these methodologies are naturalistic and appear similar, both have different methodological underpinnings. The historical, ontological, epistemological, and axiological philosophy guiding each methodology are described. In addition, the methodological underpinnings of both methodologies and a justification for use in nursing research are provided. This paper will assist future researchers who aim to employ these methodologies in nursing research.
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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 | 0.248 | 0.369 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".