Cascade training remains widely used in global health.
Cascade training can look great on paper: an expert trains a small group who, in turn, train others, thereby theoretically scaling the knowledge across an organization.
It attempts to combine the advantages of expert coaching and peer learning by passing knowledge down a hierarchy.
However, despite its promise and persistent use, cascade training is plagued by several factors that often lead to its failure.
This is well-documented in the field of learning, but largely unknown (or ignored) in global health.
What are the mechanics of this known inefficacy?
Here are four factors that contribute to the failure of cascade training
1. Information loss
Consider a model where an expert holds a knowledge set K. In each subsequent layer of the cascade, α percentage of the knowledge is lost:
- Where
is the knowledge at the nth level of the cascade. As n grows,
- Each layer in the cascade introduces a potential for misunderstanding the original information, leading to the training equivalent of the ‘telephone game’.
2. Lack of feedback
In a cascade model, only the first layer receives feedback from an actual expert.
- Subsequent layers have to rely on their immediate ‘trainers,’ who might not have the expertise to correct nuanced mistakes.
- The hierarchical relationship between trainer and trainee is different from peer learning, in which it is assumed that everyone has something to learn from others, and expertise is produced through collaborative learning.
3. Skill variation
- Not everyone is equipped to teach others.
- The people who receive the training first are not necessarily the best at conveying it to the next layer, leading to unequal training quality.
4. Dilution of responsibility
- As the cascade flows down, the sense of responsibility for the quality and fidelity of the training dilutes.
- The absence of feedback to drive a quality development process exacerbates this.
Image: The Geneva Learning Foundation Collection © 2024