Health Care Professionals' Journeys of Caring Through Portraiture
Bibliographic record
Abstract
Metropolitan Community College, a comprehensive multicampus academic institution in Omaha, Nebraska, installed portraits by the third author (MG) in the Health Careers Building and integrated them into an associate degree nursing curriculum.One goal was to expose nursing students to patients' stories in ways that encourage them to look beyond pain rating scales and protocols to the many dimensions of patients as human beings.Using portraiture in this way could be applied to any health professions curriculum, as the intersections of humanities and health care prompted students and clinicians to look beyond science and into the emotional journeys of caring.Portraiture in Nursing Curricula In an effort to integrate portraiture into a nursing curriculum, Metropolitan Community College's Department of Nursing exhibited Experience of Portraiture in a Clinical Setting (EPICS).This collection of portraits by the third author (MG), an artist-researcher, resulted from his arts-based research with patients experiencing head and neck cancers.Patients participated in the portraiture process as part of their cancer clinic visits. 1 Students in an associate degree nursing program were exposed to EPICS' 24 drawings and paintings depicting 5 patients with head and neck cancers.In September 2017, the college hosted a formal public exhibition of EPICS and a discussion with Gilbert and his fellow researchers, health care professionals, and 4 of the 5 study participants who sat for their portraits.Gilbert and one EPICS study participant also delivered guest lectures in nursing classes about portraiture and EPICS, engaging students in classroom discussions about health, illness, and the nature of interactions among art, artists, and patients during portraiture sessions.A survey was conducted to examine nursing students' responses to being exposed to the patients' portraits.This article examines how integrating portraiture into nursing education can offer an opportunity for nursing educators and students to reflect on patients' and caregivers' holistic human experience.Below, we share educators' reflections on exhibiting EPICS in a college department of nursing, along with qualitative and quantitative analyses of students' perceptions of the educational experience.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".