We think that locally developed and delivered training sessions are likely to be at least as effective as sessions delivered by "experts" whose experience may be far distant from the realities of face to face clinical work, from the cultural specificities of a particular team's setting, etc. This is in keeping with our understanding of Epistemic Trust and the Community of Practice.

Refer to the many Training exercises documented here, but create trainings that are tailored to the the needs of YOUR workforce, speaking to their condition. Some teams will want more Theory but others will want more Practice...

The following is a suggested framework for training sessions with five steps. The degree to which each of these steps applies to particular training sessions will vary.

1. Agreeing the task

This section should be brief but should ensure that the learning and training objective of the session is clear and agreed with the group. It is useful to draw out any worries about the task both as to whether it is relevant, already known about or other issues.

2. Explaining or demonstrating the training task

This can be done in a range of ways, either through some didactic teaching (or use of video teaching in the manual) or by showing material in a creative way or through role play.

3. Practice

All training sessions should provide the group to have a chance to work with the new material. For more knowledge based tasks, this may involve small group discussion etc. For skills, the group should be given a chance to practice the skill itself

4. Reflect on what has been learnt

This may overlap with practice in some cases. But for skills based learning, it is often important to enable the group to consider their different experience of practicing the skill and to consider its relevance to their day to day work.

5. Manualizing

The key principle in AMBIT is to consider how a particular skill or piece of knowledge is adapted to each local service. The process of manualizing is a process by which this is considered and local adaptations are made explicit with the possibility of further revision in the future.