<i>Sanokondu</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to describe the evolution of Sanokondu, highlighting the rationale, achievements and lessons learnt from this initiative. Sanokondu is a multinational community of practice dedicated to fostering health-care leadership education worldwide. This platform for health-care leadership education was conceived in 2014 at the first Toronto International Summit on Leadership Education for Physicians (TISLEP) and evolved into a formal network of collaborators in 2016. Design/methodology/approach This paper is a case study of a multinational collaboration of health-care leaders, educators, learners and other stakeholders. It describes Sanokondu's development and contribution to global health-care leadership education. One of the major strategies has been establishing partnerships with other educational organizations involved in clinical leadership and health systems improvement. Findings A major flagship of Sanokondu has been its annual TISLEP meetings, which brings various health-care leaders, educators, learners and patients together. The meetings provide opportunities for dialog and knowledge exchange on leadership education. The work of Sanokondu has resulted in an open access knowledge bank for health-care leadership education, which in addition to the individual expertise of its members, is readily available for consultation. Sanokondu continues to contribute to scholarship in health-care leadership through ongoing research, education and dissemination in the scholarly literature. Originality/value Sanokondu embodies the achievements of a multinational collaboration of health-care stakeholders invested in leadership education. The interactions culminating from this platform have resulted in new insights, innovative ideas and best practices on health-care leadership education.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it