Defining the experience: How do you know you are practicing Whole Person Care?
Bibliographic record
Abstract
Introduction: Whole Person Care (WPC) is a growing movement in health care recognized as an important tool for both practitioners and patient outcomes. What remains unclear is what WPC looks like in practice and how providers know they are practicing it successfully. Methods: Researchers conducted in-depth interviews with 30 healthcare providers during the WPC congress in 2017. An iterative qualitative process was used to code and analyze the data using qualitative research software.The primary research question explored how practitioners know that they are practicing WPC. Results: Our analysis revealed that the practice of WPC is an individual experiential process appreciated through self-awareness and connection. Most practitioners reported the importance of i) their ability to be present in an interaction and to acknowledge their own emotions, ii) the relationship between practitioner and patient, characterized by a feeling of trust and of mutual impact, and iii) relying on external cues, such as explicit patient feedback and body language. In this workshop, attendees will be prompted to reflect on their experiences of providing WPC through individual and group exercises. Attendees will be invited to reflect on the above results and discuss emerging themes. This participatory research model creates an interactive space for workshop attendees to build on our data and guide further analysis. Conclusion: Cues to whether you are practicing WPC rely on practitioner self-awareness and perceptions of how interactions transpire. This workshop offers an opportunity to create an experiential map of the elements contributing to the growing practice of WPC.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".