Evoking a reciprocity of caring: Integration of humanities-based content into an interprofessional education immersion course for advanced practice nursing and health professions students
Bibliographic record
Abstract
Along with calls to train nurse practitioners to be full partners in healthcare and lead interprofessional collaborative practice teams, there have been calls to enhance health professions education to develop transformative learning experiences, teach the ability to critically observe patients and capture their narrative experience of health, and assist students to become more empathic and communicate more clearly. In a Health Resources and Services Administration-funded advanced nursing education project that included advanced practice nursing, dentistry, pharmacy and social work students, several humanities-based learning strategies were infused into an interprofessional curriculum that was paired with interprofessional collaborative practice at an urban, primary care clinic serving underserved patients with multiple chronic conditions. The current study explored students’ reactions to the humanities-based interprofessional education curriculum using qualitative data from focus groups and reflective journal entries regarding student perceptions of their interprofessional collaborative practice experience in the clinical setting focused on the interprofessional education core competencies. Four major themes were identified that relate to the humanities-based curriculum. Student participants gained confidence in their abilities as health providers, improved their methods of communication and focus with patients, learned how to negotiate ethical situations in support of patient-centeredness, and grew as members of interprofessional collaborative practice teams. Thus, the inclusion of the humanities into nursing and interprofessional education and practice can assist with strengthening presence, promoting moral imagination, and evoking a reciprocity of caring that can lead to improved patient care and outcomes.
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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 | 0.004 | 0.007 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 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".