Argyris (1976) defines double-loop learning as occurring “when errors are corrected by changing the governing values and then the actions.” He contrasts this with single-loop learning, where “errors are corrected without altering the underlying governing values.”
- Double-loop learning involves questioning “not only the objective facts but also the reasons and motives behind those facts”.
- It requires becoming aware of one’s own “theories-in-use” – the often tacit beliefs and norms that shape behavior – and subjecting them to critical reflection and change.
This is challenging because it can threaten one’s sense of competence and self-image.
‘Are we doing things right?’ vs. ‘Are we doing the right things?’
In global health, double-loop learning means not just asking “Are we doing things right?” but also “Are we doing the right things?” It means being willing to challenge long-held assumptions about what works, for whom, and under what conditions.
Epistemological assumptions (“we already know the best way”), methodological orthodoxies (“this is not how we do things”), and apolitical stance (“I do health, not politics”) of epidemiology can predispose practitioners to be dismissive of a concept like double-loop learning.
Learn more: Five examples of double-loop learning in global health
Seye Abimbola is part of a growing community of researchers who argue that double-loop learning is critical for advancing equity and self-reliance in global health systems, because global health tends to overlook its own assumptions.
Is it reasonable to posit that some global health interventions have been driven by unchecked assumptions – assumptions about what communities need, what they value, and what will work in their context? How often have we relied on a one-size-fits-all approach, implementing ‘best practices’ from afar without fully understanding local realities? How do we know to what extent programs have thereby failed to meet their goals, wasted precious resources, and may have even caused unintended harm?
As Abimbola (2021) notes, “double-loop learning goes further to question and influence frameworks, models and assumptions around problems and their solutions, and can drive deeper shifts in objectives and policies.”
For example, affected communities hold vital expertise to mitigate health risks.
However, fully leveraging this potential requires global health professionals to fundamentally rethink their roles and assumptions.
- For research to serve the needs of affected communities, it is likely to be useful to reframe these roles and assumptions to see themselves as “subsidiary” partners in service of “primary” community actors (Abimbola, 2021).
- Institutionalizing double-loop learning requires enabling critical reflection and co-production between health workers, managers and citizens (Sheikh & Abimbola, 2021).
- It also depends on developing the learning capacities of communities and health workers in areas like participatory governance, team-based learning and innovation management.
The next logical question is ‘how’ to do this.
Learning culture is a critical concept missing from health systems research.
It provides a practical and actionable framework to operationalize the notion of ‘learning health systems’ and drive transformative change.
Learn more: Learning culture: the missing link in global health between learning and performance
Further reading
Learning-based complex work: how to reframe learning and development
What learning science underpins peer learning for Global Health?
How do we reframe health performance management within complex adaptive systems?
References
Abimbola, S. The uses of knowledge in global health. BMJ Glob Health 6, e005802 (2021).
Argyris, C. Single-loop and double-loop models in research on decision making. Administrative science quarterly 363–375 (1976).
Argyris, C. Double-loop learning, teaching, and research. Academy of Management Learning & Education 1, 206–218 (2002).
Kabir Sheikh & Seye Abimbola. Learning Health Systems: Pathways to Progress. (Alliance for Health Policy and Systems Research, 2021).
Image: The Geneva Learning Foundation Collection © 2024
