Burnout Among Staff in a Home Hospital Pilot
Bibliographic record
Abstract
BACKGROUND: Burnout affects large portions of the healthcare workforce and is associated with increased medical errors, decreased patient experience and adherence, loss of professionalism, and decreased productivity. Little data exists on how novel clinical care settings might impact burnout. We studied the experience and burnout of staff involved in a home hospital pilot, where acutely ill patients were cared for at home as a substitute for traditional hospitalization. METHODS: We analyzed evaluations completed by home hospital staff (physicians, registered nurses, and research assistants) at the conclusion of a 2-month pilot program. Our primary outcome was burnout evaluated by the Mini Z Burnout Survey. Secondary outcomes included overall job satisfaction, work environment, workload, and team evaluation measured on a 5-point Likert scale. RESULTS: Eight of nine (89%) staff completed evaluations. Seven of eight (88%) staff had no symptoms of burnout; one (13%) was under stress but did not feel burned out. Median overall satisfaction with home hospital was 4.5/5.0 (interquartile range (IQR), 1.0). Most staff (6/8; 75%) "strongly agreed" that their professional values were well-aligned with the program. Three of six (50%) "entirely" or "very much" preferred home hospital to their standard clinical setting. Six of eight (75%) staff felt that their opinions were "entirely" heard; four of eight (50%) felt the team "entirely" valued each of its participants. CONCLUSIONS: Novel clinical care settings like home hospital may lead to low staff burnout, high job satisfaction, and a healthy work environment. Further study is warranted.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".