Nursing Unit Communication During a US Public Health Emergency: Natural Experiment
Bibliographic record
Abstract
BACKGROUND: In the second half of 2014, the first case of Ebola virus disease (EVD) was diagnosed in the United States. During this time period, we were collecting data for the Measuring Network Stability and Fit (NetFIT) longitudinal study, which used social network analysis (SNA) to study relationships between nursing staff communication patterns and patient outcomes. One of the data collection sites was a few blocks away from where the initial EVD diagnosis was made. The EVD public health emergency during the NetFIT data collection time period resulted in the occurrence of a natural experiment. OBJECTIVE: The objectives of the NetFIT study were to examine the structure of nursing unit decision-making and information-sharing networks, identify a parsimonious set of network metrics that can be used to measure the longitudinal stability of these networks, examine the relationship between the contextual features of a unit and network metrics, and identify relationships between key network measures and nursing-sensitive patient-safety and quality outcomes. This paper reports on unit communication and outcome changes that occurred during the EVD natural disaster time period on the 10 hospital units that had data collected before, during, and after the crisis period. METHODS: For the NetFIT study, data were collected from nursing staff working on 25 patient care units, in three hospitals, and at four data collection points over a 7-month period: Baseline, Month 1, Month 4, and Month 7. Data collection was staggered by hospital and unit. To evaluate the influence of this public health emergency on nursing unit outcomes and communication characteristics, this paper focuses on a subsample of 10 units from two hospitals where data were collected before, during, and after the EVD crisis period. No data were collected from Hospital B during the crisis period. Network data from individual staff were aggregated to the nursing unit level to create 24-hour networks and three unit-level safety outcome measures-fall rate, medication errors, and hospital-acquired pressure ulcers-were collected. RESULTS: This analysis includes 40 data collection points and 608 staff members who completed questionnaires. Participants (N=608) included registered nurses (431, 70.9%), licensed vocational nurses (3, 0.5%), patient care technicians (133, 21.9%), unit clerks (28, 4.6%), and monitor watchers (13, 2.1%). Changes in SNA metrics associated with communication (ie, average distance, diffusion, and density) were noted in units that had changes in patient safety outcome measures. CONCLUSIONS: Units in the hospital site in the same city as the EVD case exhibited multiple changes in patient outcomes, network communication metrics, and response rates. Future research using SNA to examine the influence of public health emergencies on hospital communication networks and relationships to patient outcomes 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".