The Interdisciplinary Patient Partner Program: Building Better Health Care Professionals through Mentorship with Patients and Families
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Introduction: Patient- and Family Centered Care (PFCC) aims to promote collaborative empowering relationships among patients, families, and health care professionals. Best practice for teaching patient- and family-centred care is unknown. Methods, Results: Patient and Family Advisors were matched with an interdisciplinary group of 2-3 students from medicine, pharmacy, and nursing over a five month period to teach PFCC. Advisors provided their journey in the health care system as a basis for further exploration of the 4 pillars of Patient and Family Centered Care. 28 students and 14 Patient and Family Advisors completed the program. Overall, students and advisors were satisfied with the program. Attitudes toward family centeredness were evaluated on a scale of 1 (Strongly Disagree) to 5 (Strongly Agree). Paired samples t-tests were conducted to gauge perceived increases over the program. All items increased significantly with large effect sizes. Discussion: Patient and Family Advisors highlighted the importance of sharing stories and exploring them through dialogue with students as a key factor in the success of the program. The Interdisciplinary Patient Partner Program also reinforced the power of relationship as a learning tool for students. The interdisciplinary nature of this program resulted in additional learning opportunities such as learning about the interdependencies between health care professionals and the importance of an interdisciplinary approach to health care Conclusion: Matching medicine, pharmacy and nursing students with Patient and Family Advisors is an effective way to improve students' understanding of Patient and Family Centered Care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.006 |
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".