When east meets west: a qualitative study of barriers and facilitators to evidence-based practice in Hunan China
Bibliographic record
Abstract
BACKGROUND: Research into evidence-based practice has been extensively explored in nursing and there is strong recognition that the organizational context influences implementation. A range of barriers has been identified; however, the research has predominantly taken place in Western cultures, and there is little information about factors that influence evidence-based practice in China. The purpose of this study was to explore barriers and facilitators to evidence-based practice in Hunan province, a less developed region in China. METHODS: = 13). Interviews were translated into English and verified for accuracy by two bilingual researchers. Both Chinese and English data were simultaneously analyzed for themes related to factors related to the evidence to be implemented (Innovation), nurses' attitudes and beliefs (Potential Adopters), and the organizational setting (Practice Environment). RESULTS: Barriers included lack of available evidence in Chinese, nurses' lack of understanding of what evidence-based practice means, and fear that patients will be angry about receiving care that is perceived as non-traditional. Nurses believed evidence-based practice was to be used when clinical problems arose, and not as a routine way to practice. Facilitators included leadership support and the pervasiveness of web based social network services such as Baidu () for easy access to information. CONCLUSION: While several parallels to previous research were found, our study adds to the knowledge base about factors related to evidence-based practice in different contextual settings. Findings are important for international comparisons to develop strategies for nurses to provide evidence-based care.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".