Indian nurses’ views on a collaborative model of best practices: Evidence-based practice, job satisfaction, learning environment, and nursing quality
Bibliographic record
Abstract
Introduction: This study is a part of a project aimed at implementing and evaluating the Collaborative Model of Best Practice, (CMBP) to promoting evidence-based practice (EBP) in health care contexts. The aim of the study was to assess nurses’ interest, attitudes, utilisation, and views on promotors of and resources related to EBP before and after taking part in the CMBP project, and to investigate their views on the CMBP in relation to collaboration between the academy and clinical practice, the earning environment, job satisfaction, and nursing quality.Methods: A descriptive, comparative design was used with pre- and post-test measurements. The Research Utilization Questionnaire (RUQ) and study-specific questions were distributed to ward nurses (n = 67) in a rural Indian hospital.Results: Most of the nurses thought that the CMBP had a positive impact on quality of care, on their attitudes to, interest in, and knowledge EBP, and on their job satisfaction. They also considered that the collaboration between the nursing college and clinical practice had a positive impact on the learning environment and that more resources were available at the end of the project.Conclusions: The CMBP project was an attempt to improve the quality of care for patients and the learning environment for nursing students and nurses on the project wards. The results indicated fulfilment of these goals, which strengthens the usability of the model. Implementation of EBP is challenging and requires long-lasting activities and comprehensive support from leaders and facilitators. More studies are needed in which EBP is systematically implemented, accomplished, evaluated, and reported.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".