From work integrated learning to learning integrated work – A pedagogical model to develop praxis in nursing education
Bibliographic record
Abstract
The move from student to nurse has been described as difficult for newly registered nurses. Newly registered nurses’ feelings of lacking competence can reduce the opportunity to develop professional competence. Entering the nursing profession requires a high degree of adaptation. The difference between the professional competence conveyed during education and the competence demanded in working life is substantial and needs to be taken seriously. The aim of this paper is to propose a model for developing professional competence. The theoretical discussion starts with a model showing processes newly registered nurses must manage to achieve a sense of competence. These processes are highlighted by discussing how they relate to praxis in the Aristotelian tradition, situated learning and Work Integrated Learning (WIL). Learning Integrated Work (LIW) is a pedagogical approach aiming to integrate scientific knowledge with practical knowledge, and to provide an analytical perspective where students have the opportunity to develop metacognitive skills and praxis by learning in and by clinical practice experiences. One way to achieve this is to learn from the knowledge and skills used when performing practical work. The aims of WIL and LIW are to identify both practical knowledge generated by nurses in the course of their professional activities and theoretical knowledge generated in the academy, and to elaborate an understanding constituting the essence of both theoretical and practical knowledge. By integrating theoretical and practical vocational knowledge, one promotes professionalization, including the ability to perform the expected tasks and to have a critical and development-oriented attitude in daily work.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| 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".