Table 3A

Synthesis of results: determinants of national ML development 

I: Taxonomy Definition, description and classification of the concept of ML and its underlying principles
1. Meaning and focusClearly defined and described meaning and focus of ML during its developmental phases
2. Diffusion of meaning and focusLevel of (shared) understanding regarding ML and its development
II: Health system Combination of organisation, resources, financing and management
1. National approachNational strategy for ML development, based on systematic, system-wide, interprofessional and evidence-based approaches
2. Structural challengesOrganisational aspects in the healthcare system that can impede or facilitate ML development
III: Cultural aspects Characteristics and value systems of particular groups
1. Professional cultureValues, beliefs and attitudes of the medical profession, impacting their engagement in ML development
2. Societal cultureRole of public opinion and/or media in ML development
3. RecognitionRecognition of ML as part of the career structure of all doctors
4. Mind setLevel of interest in and attitude of doctors towards ML and its development
5. SubculturesPower balance between groups (eg, government vs medical profession; doctors vs managers)
6. ExposureInfluence 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 climateNational political acknowledgement of the roles of doctors and the importance ML development
2. Regulations and rulesRegulations 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. AlignmentAlignment of ML development curricula and training programmes across educational institutions
2. Standardisation and quality controlStandardising of ML development activities, identifying best practices and monitoring outcomes
3. Longitudinal and integrated trainingML development activities over extensive period of time (‘cradle-to-grave’) and based on career phase
4. Expertise of teachersClearly defined expertise and requirements for instructors, trainers and educators in ML development
5. PartnershipsInvestment in education partnerships, for example, researchers, universities and ML development providers
6. Conditions of educationConditional requirements, for example, timing, aims, duration, costs, accreditation and so on
  • ML, medical leadership.