How should health leaders approach morally contentious policy issues?
Bibliographic record
Abstract
In terms of their expertise, experience, and impact on patient care, health leaders occupy an important position in our health system. These leaders are expected to provide value to their constituents, and this value is connected to moral objectives that are fundamental to the delivery of healthcare. In some cases, leaders may interpret a certain politico-medical decision, policy, or directive to interfere with these moral objectives. In these instances, leaders can either expressly object to a decision or sideline moral views while enacting these policies or directives. We present several contemporary examples of these issues as well as the experiences of health leaders. Subsequently, we review relevant sections of the Canadian College of Health Leaders' Code of Ethics to identify existing guidance. Ultimately, we conclude that more work is needed to define the role of leaders in these circumstances, as well as the limitations of any resistance.
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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 | 0.096 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.065 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 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".