ANZAHPE LEAPS into action... Discussion!
Bibliographic record
Abstract
There are multiple leadership frameworks available for medical professionals. We have developed a Leadership Education for Australasian health Professional Students (LEAPS) framework for use in medical schools. More prominent medical professional frameworks include the: Health LEADS Australia (2013); Canadian Leads in a Caring Environment (revised in 2019), and recently published NSW Health Leadership and Management Framework (2020). The LEAPS framework has been created using transformative leadership and has student leadership skills development for all healthcare stakeholders, including the students themselves, their patients, their patients’ families and carers, and colleagues. The framework encourages students to take a leadership leap forward to build emotional agility for long-term resilience as well and learning agility for high performance. While also developing leadership skills for patient and teamwork communication, collaboration, and education. The proposed audience for this PeArLS presentation is medical student learners, health professional academics, clinicians, clinical educators, medical educators, and agencies that teach leadership skills in clinical context. Although LEAPS was created for medical education, the framework could also be relevant for other health professional professions. Purpose/Objectives The purpose of this session is to explore the complexities of this framework, the domains, competencies, and suggested learning and assessment. The main outcomes are to share ideas on how to maximise the benefit of this Framework at your medical school and explore other non-traditional medical leadership learning opportunities. Issues/Questions for exploration OR Ideas for discussion There are a few aspects raised from the presenter’s research for which broad discussion could benefit all participants. 1) How to reframe leadership training in medical or health professional education? 2) What are some short-term leadership teaching actions for immediate use? 3) What can medical students do to advocate for leadership training in their medical school?
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.343 | 0.140 |
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".