A National Scoping Study on Barriers to Conducting and Using Research Among Nurses in the United Arab Emirates
Bibliographic record
Abstract
It is important that nurses fully engage with the development and use of evidence-based practice so they can influence policy and improve patient care. There are significant challenges in developing nursing research and evidence-based practice in the United Arab Emirates (UAE). Therefore, the UAE Nursing and Midwifery Council formed a Scientific Research Subcommittee to lead the development of nursing research. Following a literature review to assess the status of nursing research in the UAE, the Subcommittee initiated a study to clarify UAE nurses' perceptions of barriers to implementing research. The results were expected to enable comparisons with other countries and establish a baseline on which to build and prioritize initiatives to address identified barriers. A cross-sectional design with convenience sampling was used to survey 606 nurses from across the UAE. The survey included the BARRIERS questionnaire and was administered online and in paper-based formats. The top three nurse-perceived barriers that affected nurses' use of research in the UAE (in descending order) were as follows: lack of authority to change patient care procedures, insufficient time to read research, and insufficient time on the job to implement new ideas. The highest ranked barriers to nurses conducting research in the UAE were lack of time and competing demands for time. The findings of this survey and a published literature review informed development of a strategy to address identified barriers to nurses in the UAE using and conducting research. This multifaceted strategy includes initiatives to reform policy and practice at local and national levels.
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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 | 0.072 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".