Accompagner les infirmières et les étudiantes dans la réflexion sur des situations de soins : Un modèle pour les formateurs en soins infirmiers
Bibliographic record
Abstract
The reform of nursing education programs and their gradual shift to a competency-based approach lays the groundwork for the introduction of active teaching strategies. These strategies focus on exposing learners to situations similar to those they will encounter in their professional practice, while encouraging them to reflect on their learning processes. This approach is akin to the process by which clinical judgment develops, which would build on reflection on past clinical experiences (Tanner, 2006). This article defines a model to support the development of clinical judgment among nurses and student nurses by having them reflect on various care scenarios. The proposed model draws on Dewey’s theory of experiential learning and reflective thinking (1909/2007, 1938/1997) and includes elements of Tanner’s model of clinical judgment in nursing (2006). An educator helps learners through the reflective process by asking open-ended questions. For learners, the reflection includes communicating their impressions of a difficult care situation, arriving at and expanding on one or more hypotheses to explain the situation, and testing relevant responses to a given situation. Throughout the process, learners are asked to reflect on how their beliefs about their nursing role, previous nursing and personal experiences, emotions and formal knowledge is impacting their response to the situation in question. This article illustrates how the model under consideration can be incorporated into different active teaching strategies, including the debriefing process associated with clinical simulations or within the context of ongoing clinical training. Research evidence shows that the model’s implementation is feasible, acceptable and beneficial and as such, an aid to learning. The strategy may also prove useful in other active learning environments, one of these being clinical placements.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".