Explaining why nurses remain in or leave bedside nursing: a critical ethnography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: To describe the application of critical ethnography to explain nurses' decisions to remain in or leave bedside nursing, and to describe researcher positioning and reflexivity. BACKGROUND: Enquiry into hospital nurses' decisions to remain in or leave bedside nursing positions has been conducted from a variety of theoretical perspectives by researchers adopting a range of methodological approaches. This research helps to explain how work environments can affect variables such as job satisfaction and turnover, but provides less insight into how personal and professional factors shape decisions to remain in or leave bedside nursing. REVIEW METHODS: A critical theoretical perspective was taken to examine the employment decisions made by nurses in a paediatric intensive care unit (PICU). DATA SOURCES: Data was collected from nurses (n=31) through semi-structured interviews and unobtrusive observation. DISCUSSION: The authors describe critical ethnography as a powerful research framework for enquiry that allowed them to challenge assumptions about why nurses remain in or leave their jobs, and to explore how issues of fairness and equity contribute to these decisions. CONCLUSION: Critical ethnography offers a powerful methodology for investigations into complex interactions, such as those between nurses in a PICU. In adopting this methodology, researchers should be sensitised to manifestations of power, attend to their stance and location, and reflexion. IMPLICATIONS FOR PRACTICE/RESEARCH: The greatest challenges from this research included how to make sense of the insider position, how to acknowledge assumptions and allow these to be challenged, and how to ensure that power relationships in the environment and in the research were attended to.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it