CLEAR: Whole Person Care Model for the Health Sciences Professions
Bibliographic record
Abstract
Whole-person care has been important throughout the 150-year history of Loma Linda University and Hospital system (now Loma Linda University Health - LLUH). Since the 1950s, the motto is, “To make man whole.” However, up to 2011 there was no corporate-wide understanding of whole-person care, or a model to guide teaching and practice. Connected to this has been the question of whether spiritual care and whole person care were similar categories of understanding.Objective / Methods: In 2011 a group of nine researchers and clinicians designed a research/development project for the purpose of developing a whole-person care model to guide all teaching and practice at LLUH. A consultation group of approximately 300 researchers, clinicians, students and staff of LLUH were invited to give feedback during the course of the project through online avenues and group forums. This larger group was open to all interested people throughout LLUH. The research / development group considered all comments, critiques and suggestions made by the larger consultation group, with all work public to both groups.Results / Conclusions: From the process emerged the first draft of a whole-person care model in March of 2012, with the following qualities: measureable, memorable, practical, flexible, and teachable. After several pilot tests, the model was adopted by LLUH as the model to guide all teaching and practice at LLUH. Since that time, the model has been integrated into the orientation of new employees (clinical and university), the teaching practices of three schools (medicine, nursing, and religion) and is being integrated into the remaining four schools by 2014. It is also guiding the development of the online wellness website for the corporation. Finally, it is used to guide two new developments for the School of Medicine: 1) a Narrative Project designed for the first year School of Medicine students, and 2) the Integrative Whole-Person Care Simulation Labs developed for second year School of Medicine students.The poster presentation will describe the whole-person care model itself, the research/development process behind it, and give examples of how it has transformed teaching and practice in the university and clinical/hospital arenas.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".