Rethinking Healthcare: Why Paradox Science Is Core to the Future of Health and Health Leadership
Bibliographic record
Abstract
Laura Desveaux1,2 1Institute for Better Health, Trillium Health Partners, Mississauga, ON, Canada; 2Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, CanadaCorrespondence: Laura Desveaux, Institute for Better Health, Trillium Health Partners, 2085 Hurontario Street, Mississauga, ON, Canada, L5B 1B8, Email Laura.Desveaux@thp.caAbstract: Solutions to healthcare’s most persistent and pervasive challenges remain elusive because we approach them as navigating oppositional tensions: the need to drive efficiency versus improve quality, to leverage cutting-edge technology versus maintain human compassion, to address population health versus providing care to the patient in front of you. The key to transforming healthcare lies in the ability of healthcare leaders to recognize when oppositional tensions are in fact paradoxes at play, to increase the capability and collective capacity to navigate them. Paradox science contends sustainable solutions to intractable challenges come not from eliminating the tensions that operate within the complexity but from the ability of those involved to hold opposing ideas in productive balance. It empowers leaders and their teams to find innovative paths by engaging with tensions directly. This perspective piece outlines three steps healthcare leaders can take to apply paradox science in practice, providing descriptions and example actions for each: 1) Clarify the paradox, 2) Encourage experimentation, and 3) Adopt a dynamic view. Moving forward, health leaders must leverage paradox science to drive forward innovation agendas in order to truly transform the healthcare experience for patients, populations, and the health workforce that serves them.Keywords: leadership, paradox theory, strategies, leading change, innovation
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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 | 0.079 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.026 | 0.047 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.014 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".