Bibliographic record
Abstract
AIM: To provide an overview of the relevance and strengths of focused ethnography in nursing research. The paper provides descriptions of focused ethnography and discusses using exemplars to show how focused ethnographies can enhance and understand nursing practice. BACKGROUND: Orthodox ethnographic approaches may not always be suitable or desirable for research in diverse nursing contexts. Focused ethnography has emerged as a promising method for applying ethnography to a distinct issue or shared experience in cultures or sub-cultures and in specific settings, rather than throughout entire communities. Unfortunately, there is limited guidance on using focused ethnography, particularly as applied to nursing research. DATA SOURCES: Research studies performed by nurses using focused ethnography are summarised to show how they fulfilled three main purposes of the genre in nursing research. Additional citations are provided to help demonstrate the versatility of focused ethnography in exploring distinct problems in a specific context in different populations and groups of people. DISCUSSION: The unique role that nurses play in health care, coupled with their skills in enquiry, can contribute to the further development of the discipline. Focused ethnography offers an opportunity to gain a better understanding and appreciation of nursing as a profession, and the role it plays in society. CONCLUSION: Focused ethnography has emerged as a relevant research methodology that can be used by nurse researchers to understand specific societal issues that affect different facets of nursing practice. IMPLICATIONS FOR PRACTICE/RESEARCH: As nurse researchers endeavour to understand experiences in light of their health and life situations, focused ethnography enables them to understand the interrelationship between people and their environments in the society in which they live.
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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 | 0.119 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".