Leadership Training Program for Medical Staff in Belgium
Bibliographic record
Abstract
Today healthcare is facing many challenges in a volatile, uncertain, complex and ambiguous environment. There is a need to develop strong leaders who can cope with these challenges. This article describes the process of a leadership training program for healthcare professionals in Belgium (named ‘Clinical Leadership Program’ or ‘CLeP’) in order to develop the future generation of strong healthcare leaders. The process over the past 3 years, refining time frame, content and format & faculty will be discussed. At the start of 2013 a literature study and round up of existing programs were made. Furthermore, a preliminary program was constructed and adapted by different stakeholders. Their feedback taken into account, CLeP was launched 6 months later. From there, 9 different hospitals and 10 groups fulfilled the program. In total 179 healthcare professionals are educated (100 physicians, 37 nurses and 42other healthcare staff). After the completion of the program, in depth interviews served as an evaluation and suggestion for further development of our program. The ‘Clinical Leadership Program’ consists of 2 modules spread over 5 days of training, each covering a different theme. These themes incorporate ‘Me’, ‘Team’, ‘Organization’, ‘Institution’ and ‘Societal impact’ respectively. The first module covers the first two days, while the second module covers day 3-5. After applying, groups of 20 participants follow the 4 months program under the guidance of a senior mentor. They can choose to follow either only the first or both of the modules and whether or not to perform an assignment within their program. In order to develop the future generation of strong healthcare leaders, CLeP was worked out. This article describes the experiences and learnings of our program over the past 3 years. In the future, similar programs will be needed even more.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".