Health LEADS Australia: implementation and integration into theory and practice
Bibliographic record
Abstract
This article reviews the development, implementation and impact of the national health leadership framework, Health LEADS Australia. While influenced by the Canadian LEADS in a caring environment approach, the Australian model had significant stakeholder engagement due to the collaborative and consensual process led by Health Workforce Australia. As stakeholder ownership has passed to formal licensees and other interested parties, adoption and adaptation has raised concerns about framework fidelity. The danger of fragmentation associated with the development of local variants is decreased by twoelements of a second wave of implementation. The incorporation of the framework into the academic curricula, where it becomes part of the existing body of knowledge, provides greater depth of intellectual resources. The development of practical resources and tools, such as a related competency framework, assists in jurisdictional implementation. Framework implementation within the Tasmanian Department of Health and Human Services and the University of Tasmania School of Medicine postgraduate program demonstrates these dynamics in this article. Mapping the relationship of Health LEADS Australia domains to mainstream leadership theory, this article contributes to the small, but growing, literature associated with this new field. The article concludes by discussing the disestablishment of Health WorkforceAustralia and the implications for the future of the Framework. The national and international connections formed during the development of Health LEADS Australia has resulted in a policy community that provides the basis for future work.
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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 | 0.074 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".