A Population Health Approach: An Organizational Case Study of Mental Models Among Hospital Leaders
Bibliographic record
Abstract
The COVID-19 pandemic thrust health systems worldwide into levels of unprecedented strain. As the pandemic waves recede, a new challenge emerges—addressing the healthcare needs of a growing population against the backdrop of historical backlogs, worsening access, and health system burnout. This reality has prompted many hospitals to revisit their strategic plans with an emphasis on modernizing healthcare delivery. Some hospitals have opted for a population health approach, which encompasses the delivery of acute care along with proactively promoting the overall health of the population. The successful execution of this approach requires aligning health system leaders’ comprehension of this approach and its operationalization. In this qualitative case study, we interviewed 13 senior leaders at a large community hospital to explore their perspectives and beliefs regarding the operationalization of a population health approach. We found varying accounts of the approach’s value, benefits, and importance, highlighting an opportunity to align leaders’ thinking. Leaders identified the organization’s low risk tolerance and decision-making structures as cultural aspects requiring evolution to support success. These findings illustrate the current state from which the organization aims to evolve and underscore the importance of identifying and aligning leaders’ underlying perspectives and beliefs as a precursor to successful implementation.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".