An Organizational Case Study of Mental Models among Health System Leaders during Early-Stage Implementation of a Population Health Approach
Bibliographic record
Abstract
Purpose: As the COVID-19 pandemic recedes, the importance of population health has come into sharp focus, prompting many health systems to explore leveraging population health data (PHD) for operational planning. This approach requires that healthcare leaders embrace the dual priorities of maintaining excellence in patient care while promoting the overall health of populations. However, many leaders are new to population-based thinking, posing a threat to successful operationalization if mental models are not aligned. Patients and Methods: This qualitative case study explored the alignment of mental models among 13 senior leaders at Trillium Health Partners (THP), one of Canada's largest community hospitals, as they embark on embedding PHD within operational workflows. Results: All leaders recognized the necessity of adopting a population health approach amid resource constraints and growing pressures. When discussing the operationalization of PHD, two distinct mental models emerged among leaders: one focused on patient care and the other on population health. While executive leaders demonstrated a fluidity in their thinking between the two, programmatic leaders favoured one over the other. For example, some viewed the organization's focus on PHD as competing with their patient care responsibilities, while others saw the use of PHD as a solution to the organization's operational pressures. Despite these divergences, leaders unanimously stressed the importance of increasing the organization's risk tolerance and devolving decision-making as a necessary precursor to realizing the transformation to a PHD-driven approach. Conclusion: These divergent mental models highlight a need to clarify the shared vision for the use of PHD along with its impact on leadership roles and accountabilities. These findings illustrate the current state from which THP aims to evolve and underscore the importance of aligning leaders' mental models as a critical step to facilitating successful integration of PHD and advancing a collective vision for healthcare transformation.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".