Bibliographic record
Abstract
Safe, effective and ethical clinical decision-making in nursing depends on critical thinking, yet there is no consensus on the educational strategies that are most effective in developing and refining this foundational skill. A qualitative inquiry among graduating Bachelor of Science in Nursing students sought to determine whether one such educational strategy, an operationalized critical thinking framework, would assist nursing students to better understand acute care patients' complex profiles. The Safe Care Framework™, consisting of the 'Concept Map Template' and the 'Priority Setting Tool Template', was developed using a constructivist pedagogical approach. The framework illustrates and operationalizes the systematic critical thinking processes that expert nurses use to provide safe, comprehensive care. Thematic analysis of qualitative survey results revealed the following three main themes; (1) greater organization and understanding of patient data; (2) guiding of assessments and priorities of care; (3) better communication with others, and several subthemes. Thus, the Safe Care Framework™ may be a practical operational tool that can support novice nurses in developing critical thinking skills. This framework adds to the literature on innovative pedagogy for nurse educators.
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 | 0.044 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".