A qualitative exploration of nurses leaving nursing practice in China
Bibliographic record
Abstract
AIM: This paper reports a theoretical understanding of nurses leaving nursing practice by exploring the processes of decision-making by registered nurses in China on exiting clinical care. BACKGROUND: The loss of nurses through their voluntarily leaving nursing practice has not attracted much attention in China. There is a lack of an effective way to understand and communicate nursing workforce mobility in China and worldwide. DESIGN: This qualitative study draws on the constant comparative method following a grounded theory approach. METHOD: In-depth interviews with 19 nurses who had left nursing practice were theoretically sampled from one provincial capital city in China during August 2009-March 2010. RESULTS: The core category 'Mismatching Expectations: Individual vs. Organizational' emerged from leavers' accounts of their leaving. By illuminating the interrelationship between the core category and the main category 'Individual Perception of Power,' four nursing behaviour patterns were identified: (1) Voluntary leaving; (2) Passive staying; (3) Adaptive staying and (4) Active staying.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".