Addressing the development of both knowledge and clinical reasoning in nursing through the perspective of script concordance: An integrative literature review
Bibliographic record
Abstract
Background: In clinical practice settings, where situations of uncertainty exist, clinical reasoning is situated or contextualized. It calls upon honed knowledge, wherein nurses rely on highly-developed and organized knowledge networks known as “mental scripts”.Methods: The aim of this integrative literature review was to address ways to develop knowledge and clinical reasoning in nursing through the use of mental scripts, and to tackle these pedagogical considerations. The literature search was performed using the following data sources: CINHAL, MEDLINE Google Scholar, PubMed, ProQuest, PsycINFO, Scopus and Web of Science.Results: Script concordance, which optimises situated clinical reasoning, ties in with the socio-cognitive perspective of cognitive apprenticeship, using role models to guide the development of knowledge and clinical reasoning in nursing. Moreover, this perspective proposes implementing new teaching strategies, which focus on situational awareness, reflective acuity, and cognitive dialogue.Conclusions: The perspective of script concordance allows a foreseeable innovative formulation of practices favourable to the development of clinical reasoning in nursing.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".