Adding a dose of empathy to healthcare: What can healthcare systems do?
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Healthcare practitioners often note system-level barriers to empathy between patients and practitioners. These include burnout-inducing administrative workloads, unfriendly meeting times, burdensome protocols, lack of wellbeing spaces, and undervaluing empathy as a core part of an institution's mission. The need for empathy in healthcare has been magnified with the current SARS-COV-2 outbreak which has limited the expression of interpersonal empathy due to rigid isolation protocols and the use of personal protective equipment. METHOD: This study-the first of its kind that we are aware of-outlines the details of a facilitated workshop run with the leadership of a tertiary level pediatric center in Canada. The workshop used a modified nominal group technique to discuss and prioritize actions to enhance empathy into the hospital system. RESULTS: Inter-professional and inter-disciplinary group of healthcare leader participants agreed on several immediately actionable steps, including embedding patient satisfaction with care measures as standard, and streamlining booking appointments. A roadmap was created to implement the other priorities. CONCLUSION: A systematic approach to infusing empathy into the structure of our healthcare system is much needed. Furthermore, inter-professional and inter-disciplinary educational workshops was well-received as a way to facilitate discussion and drive change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".