French translation and adaptation of the Lasater Clinical Judgment Rubric: A multicentre study
Bibliographic record
Abstract
Background: The clinical judgment of nursing graduates who begin practising is rarely fully adequate in situations requiring acute and critical care. Facilitating their clinical judgment development while they are learning is thus essential. Study objective: To produce a French-language version of an existing instrument for assessing the development of clinical judgment, based on translation and validation efforts in various French-speaking regions. Method: International multicentre study. Five-step transcultural translation and validation process: (1) selection of a valid and reliable English-language instrument; (2) reverse translation; (3) committee review of the translated instrument; (4) pre-testing of the translated instrument; and (5) test-retest reliability study. Results: The Lasater Clinical Judgment Rubric® has proven to be an apt instrument for reporting on the development of clinical judgment thanks both to its conceptual model and its metrological characteristics. Participants from Québec, Ontario, Manitoba, New Brunswick, Switzerland, France, and Belgium took part in the pre-testing (n=16) and test-retest (n=35) phases. The terms that proved most difficult to translate included expected patterns, patterns in data, being skillful, prompting, and commitment. Cronbach’s alpha coefficients were all greater than 0.84. In addition, all test-retest intraclass correlation coefficients were above 0.81. Conclusions: New nurses in professional practice need to have well-developed capabilities to manage the diversity and complexity of clinical situations. The French-language version of the Lasater Clinical Judgment Rubric defines the trajectory of clinical judgment development and describes the Tanner Model in concrete terms; it also facilitates communication and understanding of expectations for student performance. In clinical simulations, the Rubric may be used as a guide for debriefing, providing feedback, and encouraging reflective practice.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".