Marking out the clinical expert/clinical leader/clinical scholar: perspectives from nurses in the clinical arena
Bibliographic record
Abstract
BACKGROUND: Clinical scholarship has been conceptualised and theorised in the nursing literature for over 30 years but no research has captured nurses' clinicians' views on how it differs or is the same as clinical expertise and clinical leadership. The aim of this study was to determine clinical nurses' understanding of the differences and similarities between the clinical expert, clinical leader and clinical scholar. METHODS: A descriptive interpretative qualitative approach using semi-structured interviews with 18 practising nurses from Australia, Canada and England. The audio-taped interviews were transcribed and the text coded for emerging themes. The themes were sorted into categories of clinical expert, clinical leader and clinical scholarship as described by the participants. These themes were then compared and contrasted and the essential elements that characterise the nursing roles of the clinical expert, clinical leader and clinical scholar were identified. RESULTS: Clinical experts were seen as linking knowledge to practice with some displaying clinical leadership and scholarship. Clinical leadership is seen as a positional construct with a management emphasis. For the clinical scholar they linked theory and practice and encouraged research and dissemination of knowledge. CONCLUSION: There are distinct markers for the roles of clinical expert, clinical leader and clinical scholar. Nurses working in one or more of these roles need to work together to improve patient care. An 'ideal nurse' may be a blending of all three constructs. As nursing is a practice discipline its scholarship should be predominantly based on clinical scholarship. Nurses need to be encouraged to go beyond their roles as clinical leaders and experts to use their position to challenge and change through the propagation of knowledge to their community.
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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 | 0.034 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| 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".