Effects of a blended emergent research training programme for clinical nurses on nursing research competence and critical thinking (Part 2): A quasi‐experimental study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To evaluate the effects of a blended emergent research training programme for clinical nurses on research competence and critical thinking. BACKGROUND: With the development of the nursing discipline and evidence-based nursing practice, nursing research competence is increasingly important for clinical nurses. Current nursing research training programmes cannot meet the needs of clinical nurses. DESIGN: A quasi-experimental study. METHODS: A total of 104 clinical nurses from two tertiary hospitals participated in this study. The intervention group received a blended emergent research training programme for clinical nurses, and the control group received no intervention. Nursing research competence and critical thinking were measured at baseline, after the intervention and three months after the intervention. The satisfaction with the training of clinical nurses in the intervention group was also measured after the intervention. Generalised linear mixed models were used to evaluate the effects of the intervention on outcome variables. Qualitative data were analysed by two researchers using the content analysis method. Our reporting complies with the TREND checklist. RESULTS: The blended emergent research training programme for clinical nurses finally showed positive effects on nursing research competence and critical thinking. The effects lasted until three months after the training programme. 'Learner-centred Training', 'Skilled Trainers', 'Encouraging Training Environment' and 'Positive Outcomes' are the main categories generated from the qualitative data. CONCLUSION: The blended emergent research training programme for clinical nurses could be used for clinical nurses' research education to improve their nursing research competence and critical thinking. RELEVANCE TO CLINICAL PRACTICE: The blended emergent teaching method could be considered as an effective method to provide an encouraging learning environment and promote learner-centred teaching for clinical nurses. The blended emergent research training programme could be used to improve nursing research competence and critical thinking of clinical nurses which are important for evidence-based nursing practice.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".