Leadership development facilitated by the “sandwich” and related glaucoma fellowship programs
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to evaluate leadership training in the Sandwich Glaucoma Fellowship (SGF), a program in which fellows learn skills in a developed world institution and their home country to become leaders in glaucoma care. DESIGN/METHODOLOGY/APPROACH: This paper is a retrospective, qualitative and quantitative evaluation. Participants of the SGF between 2007 and 2019 were provided a survey eliciting demographic information, leadership training exposure, development of leadership competencies and feedback for the fellowship program. FINDINGS: Seven of nine alumni responded. The fellowship strongly impacted leadership competencies including integrity (8.8, 95% CI 7.8-9.8), work ethic (8.64, 95% CI 7.7-9.6) and empathy (8.6, 95% CI 7.7-9.5). A total of 85% of alumni indicated positive changes in their professional status and described an increasing role in mentorship of colleagues or residents as a result of new skills. Lack of formal leadership training was noted by three respondents. Informal mentorship equipped fellows practicing in regions of Sub Saharan Africa with competencies to rise in their own leadership and mentoring roles related to enhancing glaucoma management. Suggested higher-order learning objectives and a formal curriculum can be included to optimize leadership training catered to the individual fellow experience. ORIGINALITY/VALUE: Leadership is necessary in health care and specifically in the context of low- and middle-income countries to bring about sustainable developments. The SGF contains a unique "Sandwich" design, focusing on the acquisition of medical and leadership skills. This evaluation outlines successes and challenges of this, and similar fellowship programs. Other programs can use a similar model to promote the development of skills in partnership with the fellows' home country to strengthen health-care leaders.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".