Concept Mapping in a Flipped Clinical Environment: A Basic Qualitative Study
Bibliographic record
Abstract
The need to encourage critical thinking and academically engage nursing students in a clinical environment compels faculty use of assorted teaching strategies, including concept mapping and flipped learning. Though nurse educators encourage both strategies, concurrent use of both methods in clinical teaching is rare. Thus, this study examined the use of concept mapping in a flipped clinical course to encourage students’ engagement and critical thinking. Twelve baccalaureate nursing students in a second-year medical-surgical clinical course provided the data for this basic qualitative study by completing journals or diaries throughout the course and through individual semi-structured interviews at course exit. Open coding of interview transcripts and journals in conjunction with constant comparative analysis helped develop categories and themes. Several overlapping themes emerged from interview and journal data. Nursing students indicated that they developed different ways of thinking, learned from many people, became actively involved in learning and expanded their thinking, connected information, determined clinical priorities and made decisions, became confident and knowledgeable in their ability to recall information and transfer knowledge, and experienced increased critical thinking and higher level thinking skills. The results of the study showed the participants derived positive meaning from their learning in a nontraditional flipped clinical with concept mapping. Students were actively engaged in their learning and were able to expand their thinking while working collaboratively with their instructor, patients, and staff.
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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 | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".