Understanding Partnerships With Patients/Clients in a Team Context Through Verbatim Theater
Bibliographic record
Abstract
Introduction: Patient partnership has come to the forefront in health care practice and education, influencing professional programs and interprofessional education curricula. While students conceptually understand the idea of partnering with the patient, the practice of doing so is more challenging. Innovative ways to teach this health care approach may be effective in enabling students to apply their learning and promote enhanced patient partnerships. This resource provides an arts-based approach for exploring notions of partnerships with patients in a team context with interprofessional collaboration. Method: This 2-hour resource features a verbatim reader's theater script and accompanying discussion questions for a small-group reading and debrief activity. The voice of individuals with lived experience is elevated to enhance student learning and connection to the topic. Quotations were taken from interviews with individuals who had experience with the health care system and from health care providers. Results: The script and accompanying small-group discussion questions have been used in the interprofessional education curriculum with approximately 1,100 health profession students. Student response has been positive, indicating a new appreciation for thinking about partnering with patients. Discussion: Although the script has been used in the context of interprofessional education, it has the potential to be used as part of uniprofessional teaching and in practice environments, since understanding the nature of partnerships between practitioners and patients transcends all settings.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".