Objective Factors Associated with Physicians’ and Nurses’ Perceptions of Intensive Care Unit Capacity Strain
Bibliographic record
Abstract
RATIONALE: Time-varying demand for critical care may strain the capacities of intensive care units (ICUs) to provide optimal care. Intensivists and ICU nurses may be the best judges of the strain on their ICU. Yet, it is not clear what ICU and hospital factors contribute to this perceived sense of strain among ICU providers. OBJECTIVES: To identify measureable ICU and hospital factors associated with perceived strain by intensivists and ICU nurses. METHODS: During a 6-month prospective cohort study, we surveyed nurses and physicians responsible for bed management regarding the ability of a 24-bed medical ICU (MICU) to provide optimal critical care. We simultaneously assessed time-varying ICU-level factors, including patient census, number of admissions, average patient acuity, number of interhospital transfer requests, and censuses of other hospital units. To identify factors associated with strain, we used an algorithm for covariate selection in regression models that selects variables that contribute sufficiently to model prediction to justify their inclusion. MEASUREMENTS AND MAIN RESULTS: Of 254 surveys, 226 (89%) were completed by 18 charge nurses and 17 physicians. On a scale of 1 to 10 (where a higher score indicated more strain), the median perceived strain score among nurses was 6 (interquartile range, 3-7) and among physicians was 5 (interquartile range, 3-7), with moderate correlation within days (interclass correlation coefficient, 0.45; 95% confidence interval: 0.30, 0.60). Average patient acuity, MICU census, number of MICU admissions, and general ward census were included in the most efficient model of strain perceived by nurses. Only MICU census was strongly associated with strain perceived by physicians. CONCLUSIONS: A model containing commonly available metrics of ICU census, average patient acuity, and the proportion of new admissions has validity as a model of ICU nurses' perceived ICU capacity strain. However, only ICU census was associated with increased perceived capacity strain by physicians, highlighting the need for involvement of multiple stakeholder groups to improve our understanding of ICU capacity strain.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".