Nurses’ perception of barriers to research utilization in a public hospital in Saudi Arabia
Bibliographic record
Abstract
OBJECTIVES: To explore nurses` perception of barriers to research utilization. METHODS: A descriptive study was implemented. A total of 243 registered nurses in a public hospital in Riyadh, Saudi Arabia was selected using convenience sampling during the first quarter of 2013. The 29-item BARRIERS scale was used. RESULTS: The top 5 items were rated as great or moderate barriers were either setting- or nurse-related: `insufficient time to implement new ideas` (n=157, 64.6%); `nurse sees little benefit for self` (n=150, 61.7%); `nurse does not feel she/he has enough authority to change patient care procedures` (n=146 60.1%); `nurse is isolated from knowledgeable colleagues` (n=145; 59.7%); and `nurse does not have time to read research` (n=143, 58.8%). CONCLUSION: Setting- and nurse-related items comprised the top 5 barriers. Motivation issues, and knowledge-translation issues appeared to be the themes drawn from this study. Further studies using both quantitative and qualitative methods are needed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".