Building Health Services in a Rapidly Changing Landscape: Lessons in Adaptive Leadership and Pivots in a COVID-19 Remote Monitoring Program
Bibliographic record
Abstract
Adaptive leadership has become an essential skill for leaders in health systems to respond to the COVID-19 pandemic as new knowledge emerges and case counts rise, fall, and rise again. This leadership approach has been described as an iterative process of taking a wide view of the situation, interpreting the meaning of incoming data from multiple directions, and taking real-time action. This process is also common in start-ups, which attempt to create new products or services of uncertain value for consumer markets that may not yet exist. Start-ups manage uncertainty through "pivots," which can include changes in the target group, need, features, or intended benefit of a product or service. Pivots are large changes that account for the high likelihood of getting something wrong during development, and they are distinct from the "tweaks" or small tests of change that define quality improvement methodology. This case study describes three pivots in the launch of a remote monitoring program for COVID-19. Adaptive leadership helped inform strategic decisions, with pivots providing a framework for internal and external stakeholders to articulate options for changes to address shifting needs. There is considerable uncertainty in the appropriate design and implementation of health services, and although this case example focuses on the use of adaptive leadership and pivots during a pandemic, these strategies are relevant for health care leaders at any time.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".