A novel research competency framework for clinical research nurses and midwives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 it