Training interprofessional faculty in humanism and professionalism: a qualitative analysis of what is most important
Bibliographic record
Abstract
Introduction: The capacity of healthcare professionals to work collaboratively influences faculty and trainees’ professional identity formation, well-being, and care quality. Part of a multi-institutional project*, we created the Faculty Fellowship for Leaders in Humanistic Interprofessional Education at Boston Children’s Hospital/ Harvard Medical School. We aimed to foster trusting relationships, reflective abilities, collaboration skills, and work together to promote humanistic values within learning environments. Objective: To examine the impact of the faculty fellowship from participants’ reports of “the most important thing learned”. Methods: We studied participants’ reflections after each of 16 1½ hour fellowship sessions. Curriculum content included: highly functioning teams, advanced team formation, diversity/inclusion, values, wellbeing/renewal/burnout, appreciative inquiry, narrative reflection, and others. Responses to “What was the most important thing you learned?” were analyzed qualitatively using a positivistic deductive approach. Results: Participants completed 136 reflections over 16 sessions–77% response rate (136/176). Cohort was 91% female; mean age 52.6 (range 32-65); mean years since completion of highest degree 21.4; 64% held doctorates, 36% master’s degrees. 46% were physicians, 27% nurses, 18% social workers, 9% psychologists. 27% participated previously in a learning experience focusing on interprofessional education, collaboration or practice. Most important learning included: Relational capacities/ Use of self in relationships 96/131 (73%); Attention to values 46/131 (35%); Reflection/ Self-awareness 44/131 (34%); Fostering humanistic learning environments 21/131 (16%). Discussion: Results revealed the importance of enhancing relational capacities and use of self in relationships including handling emotions; attention to values; reflection/self-awareness and recognition of assumptions; and fostering humanistic learning environments. These topics should receive more emphasis in interprofessional faculty development programs and may help identify teaching priorities. *Supported in part by a multi-institutional grant from the Josiah Macy, Jr. Foundation (Dr. Branch as PI; Dr. Rider as site PI).
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".