Nurse teachers’ perceived competencies in the context of students’ first clinical placements: A qualitative study
Bibliographic record
Abstract
This study seeks to illuminate the competencies of nurse teachers (NTs) and their operationalization in the context of clinical placement by exploring the challenges of being an NT, as experienced and articulated by diverse groups of interacting agents: NTs, mentors, and students. To gain insight into this area, we employed an interpretative qualitative approach, and applied data source and methodology triangulation: Focus group discussions with nurse mentors and students and e-mail interviews with NTs responsible for the placement learning were performed. Five main themes were revealed: NTs’ personal and professional mastery, mastery of student support, mastery of mentor support, mastery of learning/teaching environment, and mastery of conditions while in the clinical placement. In addition, NTs emerged as coordinators, mediators, and moderators of a complex system. Within this system, the complex interplay of diverse components can have various facilitating or obstructing effects. Considering this complexity, we argue that part of those effects is directly connected to individual NTs’ characteristics, combination of professional competencies, and application of these competencies in specific situations. We also propose that institutional and departmental contexts, as well as professional contexts of nursing practice and education, influence both teachers and students. Our research draws attention to the further development of organized and structured cooperation within and across institutions in establishing and maintaining links among different contexts of nursing education. With regard to placement learning, the complementary competencies of NTs and mentors, which mesh across fields and domains of expertise, appear to be a possible solution.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".