Translation and adaptation of a clinical judgment model for nursing education and research in a francophone context
Bibliographic record
Abstract
To pursue the development of the science and practice of nursing education, the dissemination of knowledge in French about learning to think like a nurse and how to facilitate this learning remains an important issue. This article presents the French translation, adaptation, and validation of Tanner's (2006) Model of Clinical Judgment in nursing. A four-step process of translation, back-translation, and validation was conducted according to the recommendations of Sousa and Rojjanasrirat (2011). The French version of the model was validated by 10 nursing education experts and by its original author. The model defines clinical judgment as an understanding, interpretation, or conclusion about a person's health needs, concerns, or problems. It describes four interrelated aspects of clinical judgment that can apply to rapidly changing care situations with ambiguous or ill-defined parameters: noticing, interpreting, responding, and reflecting. In addition to describing the clinical judgment of nurses with different levels of expertise, this model is an important tool to guide nursing education research and design educational experiences for nurses and nursing students. It is also a relevant tool for assessment and mentoring.
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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 | 0.046 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".