Global health care leadership development: trends to consider
Bibliographic record
Abstract
Maura MacPhee,1 Lilu Chang,2 Diana Lee,3 Wilza Spiri4 1University of British Columbia School of Nursing, Vancouver, British Columbia, Canada; 2Center for Advancement of Nursing Education, Koo Foundation, Sun Yat-Sen Cancer Center, Taipei, Taiwan; 3Nethersole School of Nursing, Chinese University of Hong Kong, Hong Kong, 4São Paulo State University, Botucatu, São Paulo, Brazil Abstract: This paper provides an overview of trends associated with global health care leadership development. Accompanying these trends are propositions based on current available evidence. These testable propositions should be considered when designing, implementing, and evaluating global health care leadership development models and programs. One particular leadership development model, a multilevel identity model, is presented as a potential model to use for leadership development. Other, complementary approaches, such as positive psychology and empowerment strategies, are discussed in relation to leadership identity formation. Specific issues related to global leadership are reviewed, including cultural intelligence and global mindset. An example is given of a nurse leadership development model that has been empirically tested in Canada. Through formal practice–academic–community collaborations, this model has been locally adapted and is being used for nurse leader training in Hong Kong, Taiwan, and Brazil. Collaborative work is under way to adapt the model for interprofessional health care leadership development. Keywords: health care leadership, development models, global trends, collective
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".