Bibliographic record
Abstract
The field of health leadership is shifting rapidly, and there is an opportunity to learn with health leaders about what is needed to support health leadership education, research, and practice. In 2022, to augment student feedback and faculty praxis, Royal Roads University conducted 12 virtual interviews with senior health system leaders across various settings to learn how health leaders can better respond to emerging and future leadership needs and priorities facing health systems. Findings from this study informed the development of a health-specific elective for the Master of Arts in Leadership, Health Specialization program entitled Considerations for Health Systems Renewal. This elective explores the following topics that emerged from this research study: (1) an orientation to possibility; (2) emerging strategic human resource concerns; (3) healthcare innovation; (4) relational and social systems leadership; (5) polarity thinking; (6) trauma-informed leadership; and (7) Canadian healthcare networks. In this article, we share our research process and findings to arrive at these recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".