INTERDISCIPLINARY AND CROSS-CULTURAL LITERACY: AN ACTIVITY INCREASING BUSINESS AND NURSING STUDENTS CULTURAL AND HEALTH CARE INDUSTRY AWARENESS
Bibliographic record
Abstract
This article describes an experiential learning assignment encompassing an activity used by three faculty members in two different academic disciplines, Nursing and International Business. This assignment has proven to be mutually beneficial for students and teachers in achieving their course learning objectives. This assignment can be used as a template for other instructors interested in cross cultural and / or cross disciplinary collaboration. In its current form, this assignment involves a 300 level course for students studying International Business and a 200 level course for nursing students studying Relational Practice and communication with others. The activity concerns the assigning of students into small working groups whose members are representative of different cultures and different academic disciplines. This mission requires students to meet (out of regular class time) to discuss and share their knowledge and perceptions of their own culture and the health care industry in their home countries. Students may participate in this activity regardless of their level of knowledge regarding the other's culture. Participating in the activity provides students the opportunity to discuss characteristics of their own culture and country and to learn about other countries from fellow students. The activity encourages break downs in stereotyping, and to generate confidence in communicating with others that may seem 'different'.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".