Enhancing system empathy within a UK Emergency Department: A feasibility interprofessional priority setting exercise
Bibliographic record
Abstract
ABSTRACT Background System-level barriers inhibit empathy in healthcare, and this can harm patients and practitioners. The barriers include burnout-inducing administrative workloads, burdensome protocols, lack of wellbeing spaces, un-empathic leadership, and not emphasising empathy as an institutional value. A workshop aimed at enhancing empathic systems was successfully delivered in Canada but has not been tested in the UK National Health Service (NHS) setting. Aim To test the feasibility of an empathic systems workshop within the UK NHS setting. Methods We conducted an interprofessional group of an emergency department (ED). We used a modified nominal group technique to prioritise actions to enhance empathy in the ED system. Satisfaction with the workshop and confidence that the workshop would lead to change were measured on a 10-point Likert scale. Results Twenty-eight participants representing the following stakeholder groups attended the workshop: leaders, consultants, nurses, security, and porters. The group agreed to generating a better wellbeing action plan and implementing an effective secondary triage system. Seventy-three percent (73%) rated their satisfaction with the workshop as 8 or higher out of ten, and 63% reported being confident that the workshop would lead to positive change. Limitations A doctors strike limited the range of stakeholders who were able to attend, and long-term follow up was not conducted. Conclusions Participants in a UK setting were satisfied with a previously developed system empathy workshop and reported being confident that it would lead to positive change. Participants were able to prioritise changes that would improve system empathy and were confident that the changes would be effective.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".