Patients as educators: Contemporary application of an old educational strategy to promote patient-centered care
Bibliographic record
Abstract
Patients first. Patient-centered care. Patient-centered medical homes. The patient experience. Today, it is hard to miss the appeal for patient-centered care in US health care reform, as well as in national and professional publications. If patients are the focus, shouldn’t they formally contribute to nursing and other health professions education? The earlier in their education students understand patients’ perspectives, the better they can integrate patients’ reality into their practice. Experiencing the health care maze through the perspective of a patient or family living with disability, cancer, chronic disease, or dementia has an indelible impact on a learner’s practice. While simulation, standardized patients, and problem- based learning are excellent educational strategies, patients as teachers in the classroom creatively, efficiently, and memorably bring life to multiple concepts and content areas far more effectively than an abstract case, lecture, and/or bulleted slides. This article presents a brief history of patient teachers, and the authors’ experiences of integrating them into the nursing curriculum. Students enjoyed learning, participated enthusiastically, and evaluated these classes at the highest level. Patients found teaching empowering and were proud to teach future nurses.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".