A leadership ethics curriculum: Bringing mixed-methods interdisciplinary insights to the ethical complexities of health leadership
Bibliographic record
Abstract
In response to the increasingly complex ethical issues facing health leaders, the Bioethics Department at The Hospital for Sick Children (a Canadian quaternary care paediatric research institution) was asked by senior leadership to develop a leadership ethics curriculum that would further develop the ability of its institution's leaders to deliberate and make morally defensible decisions in their roles. Insights from an interdisciplinary literature review suggest that the general objectives and structure of leadership ethics teaching remain constant, with specifics changing depending on the organization and intended participants. Implementing findings from an institutional needs assessment, our modular leadership ethics curriculum, which engages participants in asynchronous and synchronous learning, was designed to support (1) understanding of personal and organizational values, (2) recognizing the significance of attending to the ethical dimensions of decisions, (3) familiarity with leadership and organizational expectations, and (4) practicing application of ethical analysis, enhancing abilities and confidence to engage with ethical issues.
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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 | 0.028 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".