Development of a blended emergent research training program for clinical nurses (part 1)
Bibliographic record
Abstract
Abstract Background Nursing research training is important for improving the nursing research competencies of clinical nurses. Rigorous development of such training programs is crucial for ensuring the effectiveness of these research training programs. Therefore, the objectives of this study are: (1) to rigorously develop a blended emergent research training program for clinical nurses based on a needs assessment and related theoretical framework; and (2) to describe and discuss the uses and advantages of the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) in the instructional design and potential benefits of the blended emergent teaching method. Methods This intervention development study was conducted in 2017, using a mixed-methods design. A theoretical framework of blended emergent teaching was constructed to provide theoretical guidance for the training program development. Nominal group technique was used to identify learners’ common needs and priorities. The ADDIE model (Analysis, Design, Development, Implementation, Evaluation) was followed to develop the research training program for clinical nurses based on the limitations of current nursing research training programs, the needs of clinical nurses, and the theoretical foundation of blended emergent teaching. Results Following the ADDIE model, a blended emergent research training program for clinical nurses to improve nursing research competence was developed based on the needs of clinical nurses and the theoretical framework of blended emergent teaching. Conclusions This study indicates that nominal group technique is an effective way to identify learners’ common needs and priorities, and that the ADDIE model is a valuable process model to guide the development of a blended emergent training program. Blended emergent teaching is a promising methodology for improving trainees’ learning initiative and educational outcomes. More empirical studies are needed to further evaluate blended emergent teaching to promote the development of related theories and practice in nursing education.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".