A novel research competency framework for clinical research nurses and midwives
Bibliographic record
Abstract
BACKGROUND: Clinical research nurses and midwives (CRN/Ms) are highly specialised registered nurses. They combine their clinical nursing expertise with research knowledge and skills to aid in the delivery of rigorous, high-quality clinical research to improve health outcomes, the research participant's experience and treatment pathways ( Beer et al 2022 ). However, there is evidence that the transition into a CRN/M role is challenging for registered nurses. AIM: To discuss the development of a competency framework for CRN/Ms. DISCUSSION: The authors identified a gap in their organisation for standards that would support the development of CRN/Ms new to the role. The standards needed to be clear and accessible to use while encompassing the breadth of scope of CRN/Ms' practice. The authors used a systematic and inclusive process drawing on Benner's ( 1984 ) theory of competence development to develop a suitable framework. Stakeholders engaged in its development included research participants, inclusion agents and CRN/Ms. CONCLUSION: The project identified 15 elements that are core to the CRN/M role and the knowledge, skills and behaviours associated with it. IMPLICATIONS FOR PRACTICE: A large NHS trust has implemented the framework. It is also being shown to national and regional networks. Evaluation is under way.
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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 | 0.063 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".