Cognitive Companionship: A Potential Pedagogical Approach to Developing Clinical Reasoning in Nursing?
Bibliographic record
Abstract
The cognitive and metacognitive processes favoured by experts in real practice situations are rarely evoked or called upon in nursing education. Students, in fact, are only exposed to these concepts very rarely or sporadically, if at all. As a result, the tacit knowledge acquired by experts is too often kept under wraps, effectively preventing students from obtaining explanations, reasons or clear precisions regarding the clinical assumptions and interventions discussed by experts during real practice situations. This begs the question of how to reap the benefits of the clinical reasoning adopted by experts to design a companionship method for improving student learning in the area of nursing competency. This article explores cognitive companionship as an educational approach to developing such clinical reasoning. More specifically, theoretical and epistemological foundations are discussed in detail to allow for a better understanding of the educational principles underlying the application and possible contribution of cognitive companionship to competency development. The content of this article consists of a thorough conceptualization of the theoretical underpinnings of the educational strategies to incorporate into nursing education programs, in the hopes that they will favour the development of clinical reasoning in nursing. The authors have identified and expanded upon various educational strategies for preparing academic and clinical settings for the eventual use of cognitive companionship. These strategies illustrate the uses of cognitive companionship in various learning/teaching situations (specifically, preceptorship programs, clinical simulations or learning through e-learning environments). Rooted in a socio-cognitive approach, they rely on the articulation of knowledge, reflection, and a cognitive dialogue that is both socially shared and linked to real practice situations. Cognitive companionship makes it possible to understand the living knowledge of experts, a specialized knowledge that grows with practice, but which is hard to put to paper in reference works. Cognitive companionship also relies on an approach suitable to the development of clinical reasoning in nursing. Ultimately, this approach could foster a professional development culture supported by an entire learning community. The authors suggest carrying out research and academic activities to identify a consistent link between the cognitive companionship approach, the related educational strategies and development of the desired competence.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".