Table 3A

Synthesis of results: determinants of national ML development

(Sub)categories	Description
I: Taxonomy	Definition, description and classification of the concept of ML and its underlying principles
1. Meaning and focus	Clearly defined and described meaning and focus of ML during its developmental phases
2. Diffusion of meaning and focus	Level of (shared) understanding regarding ML and its development
II: Health system	Combination of organisation, resources, financing and management
1. National approach	National strategy for ML development, based on systematic, system-wide, interprofessional and evidence-based approaches
2. Structural challenges	Organisational aspects in the healthcare system that can impede or facilitate ML development
III: Cultural aspects	Characteristics and value systems of particular groups
1. Professional culture	Values, beliefs and attitudes of the medical profession, impacting their engagement in ML development
2. Societal culture	Role of public opinion and/or media in ML development
3. Recognition	Recognition of ML as part of the career structure of all doctors
4. Mind set	Level of interest in and attitude of doctors towards ML and its development
5. Subcultures	Power balance between groups (eg, government vs medical profession; doctors vs managers)
6. Exposure	Influence of doctors in key positions (eg, in national politics or management)
IV: Governance	Establishment of relevant policies and monitoring of their proper implementation
1. Political climate	National political acknowledgement of the roles of doctors and the importance ML development
2. Regulations and rules	Regulations requiring doctors to be active in management or to engage in (periodical, obligatory) ML development
V: Education	Representation of ML in undergraduate and postgraduate medical education and training
1. Alignment	Alignment of ML development curricula and training programmes across educational institutions
2. Standardisation and quality control	Standardising of ML development activities, identifying best practices and monitoring outcomes
3. Longitudinal and integrated training	ML development activities over extensive period of time (‘cradle-to-grave’) and based on career phase
4. Expertise of teachers	Clearly defined expertise and requirements for instructors, trainers and educators in ML development
5. Partnerships	Investment in education partnerships, for example, researchers, universities and ML development providers
6. Conditions of education	Conditional requirements, for example, timing, aims, duration, costs, accreditation and so on

ML, medical leadership.