Planning, Designing, Building, and Moving a Large Volume Maternity Service to a New Labor and Birth Unit
Bibliographic record
Abstract
Three teams of perinatal expert nurses participated in planning and designing a new maternity unit, operationalizing the move to the new space, and evaluating care processes and workflows after the move. The hospitals involved were University of California, San Francisco Benioff Children's Hospital, Prentice Women's Hospital of Northwestern Memorial Healthcare in Chicago, IL, and Florida Hospital Orlando, Florida Hospital for Women. Although each team discussed specific details and lessons learned, there is remarkable consistency among the experiences of these teams and with the discussion of the process by the team at Mercy Hospital St. Louis published in this issue of MCN The American Journal of Maternal Child Nursing. Extensive planning, flexibility, involving key stakeholders, evaluating and simulating workflows, and adequate staffing and patient safety on move-day were reported to be essential to success. Reevaluation after settling in to the new unit and making changes as needed were discussed. Being part of the leadership team involved in planning and moving to a new maternity unit in what was likely a once-in-a-lifetime experience was viewed as a career highlight. Their commentary adds to what is known about planning and designing new maternity units, moving into the new space, and adjusting unit operations and care after making the new unit home.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".