Bibliographic record
Abstract
PURPOSE: The purpose of this article was to describe a process followed in developing and evaluating a model to facilitate authentic learning (AL) in nursing education. METHOD: A qualitative and theory generative research designs were used to develop the model. The four steps of theory generative research design namely concept analysis, construction of conceptual relations, the description of a model and the description of the guidelines for operationalisation were employed. The model was then developed and evaluated. DISCUSSION: A model development was done based on the following structure: (1) an overview of the model; (2) the purpose of the model; and (3) the structure of the model, which further includes the following: (3.1) the assumptions of the model, (3.2) the concept definitions, (3.3) the relational statements, and (3.4) the nature of the structure; as well as (4) the process description. A schematic presentation, which depicts the six elements of practice theory namely the context, agent, recipient, dynamic, process and procedure, and terminus or outcome of AL in nursing education was shown. CONCLUSION: The described model is a framework that can be used to guide nurse educators in educating, training and producing a 21st century graduate who has higher order thinking skills, make astute clinical reasoning, judgment and rational decisions therefore will be able to deliver comprehensive, holistic care in line with the dynamic, highly-demanding interdisciplinary global healthcare system.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".