Nursing clinical reasoning cognitive strategies used in a learning-by-concordance modality: A qualitative descriptive study
Bibliographic record
Abstract
AIMS: The primary aim of this study is to describe the cognitive strategies employed by nursing students during a Learning-by-Concordance (LbC) activity. A secondary aim is to compare these strategies with those used by experienced nurses. BACKGROUND: Technological advancements have facilitated the integration of diverse pedagogical modalities into nursing education. However, the mechanisms by which digital modalities support the development of cognitive strategies for clinical reasoning remain insufficiently understood. DESIGN: A descriptive qualitative design was adopted. METHODS: Content analysis was used to identify and describe the cognitive strategies mobilized during the LbC activity. This was complemented by a frequency analysis to compare the strategies used by students and experienced nurses. RESULTS: A total of 46 participants were recruited: 10 novice students, 16 intermediate students and 20 experienced nurses, including 13 rehabilitation nurses and 7 nurse educators. Findings revealed that some cognitive strategies-such as identifying salient data, seeking additional information and forming relationships between data-were more frequently employed. Student responses varied in precision and length, while nurses' responses tended to include detailed contextualization of nursing hypotheses. Educators' responses often emphasized procedural rules and provided clarifications related to the proposed hypotheses. CONCLUSIONS: The results underscore the importance of adapting the LbC modality to better support the cognitive strategies essential for clinical reasoning in nursing. Three pedagogical variations are proposed: (1) structuring activities around illness and nursing scripts; (2) integrating think-aloud strategies; (3) fostering interactivity through individual and collaborative group work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".