The improvement in research orientation among clinical nurses in Qatar: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The main barrier to engaging nurses in research is the lack of research knowledge and skills. AIM: To explore the influence of research workshops on the research orientation of nurses in a large referral hospital in Qatar. DISCUSSION: This article describes a cross-sectional study involving 564 nurses working in 14 health facilities who attended research workshops in Qatar. The authors collected data using the Edmonton Research Orientation Survey (EROS) as well as questions considering support and barriers to research. Descriptive statistics were used to summarise and determine the sample characteristics and distribution of participants. The participants who attended the workshop were found to have a higher orientation towards the EROS sub-scales of evidence-based practice, valuing of research, involvement in research, being at the leading edge of the profession and support for research, compared to those who did not attend the workshop. There was no statistical difference between the groups in the research barrier sub-scale. CONCLUSION: Despite significant improvements in their responses to the EROS research orientation sub-scales after attending the workshop, the nurses still reported many barriers to being actively engaged in research. IMPLICATIONS FOR PRACTICE: Healthcare organisations should assist with integrating evidence-based practice into healthcare. There is a need for research education for clinical nurses to bring evidence into clinical practice to improve the quality of patient outcomes. Increasing the research capacity of nurses will lead to their emancipation in addressing the flaws in clinical practice and motivate them to use evidence in patient 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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".