Small Patients but a Heavy Lift
Bibliographic record
Abstract
OBJECTIVE: This study explored the association between workload and the level of burnout reported by clinicians in our neonatal intensive care unit (NICU). A qualitative analysis was used to identify specific factors that contributed to workload and modulated clinician workload in the NICU. STUDY DESIGN: We conducted a study utilizing postshift surveys to explore workload of 42 NICU advanced practice providers and physicians over a 6-month period. We used multinomial logistic regression models to determine associations between workload and burnout. We used a descriptive qualitative design with an inductive thematic analysis to analyze qualitative data. RESULTS: Clinicians reported feelings of burnout on nearly half of their shifts (44%), and higher levels of workload during a shift were associated with report of a burnout symptom. Our study identified 7 themes related to workload in the NICU. Two themes focused on contributors to workload, 3 themes focused on modulators of workload, and the final 2 themes represented mixed experiences of clinicians' workload. CONCLUSION: We found an association between burnout and increased workload. Clinicians in our study described common contributors to workload and actions to reduce workload. Decreasing workload and burnout along with improving clinician well-being requires a multifaceted approach on unit and systems levels.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".