Bringing Our Whole Person to Whole Person Care: Fostering Reflective Capacity with Interactive Reflective Writing in Health Professions Education
Bibliographic record
Abstract
Reflective learning and practice foster personal, professional, and interprofessional identity development within health professions education to encourage humanistic, competent patient care. Reflection on experience nurtures mindful presence and adaptive expertise/”practical wisdom,” enabling the health care professional to recognize and address patients’ and families’ emotional, psychosocial, cultural, and spiritual needs for optimizing whole person care (WPC). By heightening awareness of strengths, values, biases, and/or limitations, reflection also helps the provider bring more of his/her “whole person” to WPC, strengthening the provider-patient therapeutic relationship (reciprocity for healing). The use of reflective writing (RW) to augment reflective practice is well documented. RW in the small group setting fosters narrative competence (hearing/responding to a patient’s story, awareness of one’s own stories), self-assessment, moral sensitivity, empathy, emotional processing, and provider well-being. At Alpert Med, we have implemented an “interactive reflective writing” (IRW) paradigm of guided individualized feedback from interdisciplinary faculty to students’ RW in a Doctoring course and Family Medicine clerkship (with small group peer-based narrative sharing and collaborative feedback). Frameworks for enhancing educational value of feedback (BEGAN and REFLECT-reflective level evaluation rubric) were developed, incorporated into student and faculty guides, and applied in faculty development.INTERACTIVE WORKSHOP OBJECTIVES: 1) Participants will be familiarized with constructs of reflective learning/practice and IRW, 2) Participants will apply BEGAN and REFLECT to a student’s reflective essay as exemplar, 3) Participants will engage in interactive dialogue with medical student presenters on positive learning outcomes of IRW for WPC 4) Participants will consider and share merits, limitations, and possible utility of presented curricula/evaluative tools for their settings. FORMAT/ACTIVITIES:1. Didactic - Reflective Learning/Practice for WPC2. Participants provide feedback to student’s RW3. Discussion4. Introduce BEGAN/REFLECT frameworks5. Participants re-craft feedback with frameworks/Discuss6. Student/faculty presenters share experiences of IRW pedagogy for fostering reflection and WPC7. Wrap-up/Q and A.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".