Caring for the healthcare professional
Bibliographic record
Abstract
Purpose Hospitals must systematically support employees in innovative ways to uphold a culture of care that strengthens the system. At a leading Canadian academic pediatric rehabilitation hospital, over 90 percent of clinicians viewed Schwartz Rounds™ (SR) as a hospital priority, resulting in its formal implementation as a quality improvement initiative. The purpose of this paper is to describe how the hospital implemented SR to support the socio-emotional impact of providing care. Design/methodology/approach This quantitative descriptive study provides a snapshot of the impact of each SR through online surveys at four assessment points (SR1-SR4). A total of 571 responses were collected. Findings All four SR addressed needs of staff as 92.9-97.6 percent of attendees reported it had a positive impact, and 96.4-100 percent of attendees reported each SR was relevant. Attendees reported significantly greater communication with co-workers after each SR ( p<0.001) and more personal conversations with supervisors after SR2 and SR4 ( p<0.05) compared to non-attendees. Attending SR also increased their perspective-taking capacity across the four SR. Practical implications As evidenced in this quality improvement initiative, SR addresses staff's need for time to process the socio-emotional impacts of care and to help reduce those at risk for compassion fatigue. SR supports and manages the emotional healthcare culture, which has important implications for quality patient care. Originality/value This research details an organization's process to implement SR and highlights the importance of taking care of the care provider.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".