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 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 | 0.044 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".