Healthcare Provider Strike Preparedness and Response: Lessons Learned from Physician Strikes in New York City
Bibliographic record
Abstract
Labor actions by healthcare workers are increasing in frequency and quantity, particularly throughout the United States. Regardless of their cause and size, these strikes have the potential to disrupt normal hospital operations and could impact patient access to care, quality of care, and costs. Strikes resemble other large-scale incidents like natural disasters, pandemics, or terrorist attacks in that they shrink a hospital’s capacity to care for patients, force hospitals to pursue logistically complicated actions like finding replacement providers, and impact nearby facilities due to the offloading of patients. In contrast to these incidents, however, strikes are unique because they often come with months of advanced notice, they reduce capacity by precise amounts with predictable provider losses, they occur over defined periods of time, and they do not necessarily increase the demand for patient care. To maximize efficiency and minimize disruption in response to strikes, hospitals must properly plan ahead and successfully execute their plans. Drawing on the recent planning and response to a resident physician strike at a New York hospital, this paper recounts the experience while describing six core strategies and a planning template that other hospitals can use to prepare for and respond to healthcare provider strikes. These strategies include strike aversion, increasing coverage, decreasing demand, internal and external messaging, creating external partnerships, and demobilization. When properly planned for using Appendix A: Strike Planning Template, strike consequences can be mitigated to ensure that patient care and hospital operations can continue with minimal impact to access, quality, or cost.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".