There is a good reason complex, multi-stakeholder problems have generated such a rich body of methodology over the last thirty years. Health systems, workforces and social policy are exactly the kind of environment these methods were built for: many actors, no single authoritative account of the problem, and a genuine risk of doing real harm by acting on the wrong diagnosis. Evaluators, funders and researchers have spent decades building serious, well-tested tools for exactly this situation.
The trouble is that almost none of those tools made it into general use. Ask a room of experienced health or workforce leaders to define a logic model, a Theory of Change and a realist synthesis, and most will recognise the terms. Ask them to actually build one, unprompted, on a real problem they’re facing this week, and the room usually goes quiet. The gap between a genuinely useful body of knowledge and what practitioners can actually pick up and use is where PEASI came from. PEASI is not proposed as an alternative to this literature, but as a distillation of it, arrived slowly by using the heavier versions on real problems for the better part of two decades, and paying attention to what kept working.
A crowded field, for good reason
Researchers who have tried to catalogue this field have found there are now so many named theories, models and frameworks for guiding change that choosing between them has become its own research problem (Nilsen, 2015). That’s not a criticism of the field — it reflects how many genuinely different sub-problems sit inside “how do we make sense of complex change.” Some of these frameworks focus on planning, some on evaluation, some on explaining why an intervention succeeded or failed, and some on implementation specifically. Three of them sit closest to PEASI’s own ancestry, and are worth taking seriously on their own terms before asking what a simpler synthesis needs to keep.
Programme logic: the architecture of “if we do X, in context Y, we expect Z”
The W.K. Kellogg Foundation’s Logic Model Development Guide (Kellogg, 2004), is one of the most widely used practical formulations of programme logic, often resulting in a ‘logic model’ which formalised a chain that has become close to standard in public health and social policy: resources and inputs, activities, outputs, and outcomes or impact. Its real contribution isn’t the chain itself, but the discipline it forces: it makes an organisation’s implicit theory of why an activity should produce a result explicit enough to be examined, tested, and supports diagnoses when things go wrong. A programme logic doesn’t guarantee an initiative will work. It guarantees that when it doesn’t, you should be able to find out why: whether the activity was wrong, the context was unsupportive, or the underlying assumption never held.
What programme logic has struggled with is scale and heterogeneity. Standard logic models were built for single-setting, single-programme evaluation. They start to strain when a funder or a health system needs to make sense of dozens of different projects, in different settings, run by different professions, all nominally testing the same idea. That was the exact problem behind a large-scale evaluation I led in 2013, synthesising the outputs of 55 allied-health workforce redesign projects across Queensland Health, spanning more than 500 staff and thirteen disciplines (Nancarrow et al., 2013). No existing logic-model method was designed to be tested, transparently and reproducibly, against 122 project documents, 84 surveys and a round of stakeholder interviews spanning that much variation. Building a method that could — logic models used not just to plan, but to generate and empirically test propositions — turned out to be the direct methodological ancestor of PEASI’s causal chain.
Realist synthesis and realist evaluation: “what works, for whom, under what circumstances”
Where programme logic asks whether the causal chain holds, realist evaluation asks a sharper question: not does this intervention work, but what is it about this intervention that works, for which people, in which contexts, and why (Pawson & Tilley, 1997). Its central move is the context-mechanism-outcome configuration — the idea that the same nominal intervention can trigger a mechanism that produces the intended outcome in one setting, and produce nothing, or the opposite, in another. Realist approaches take context extremely seriously, which is exactly what makes them powerful and exactly what makes them slow to apply.
I used realist principles across two projects that shaped PEASI directly. One was a large UK evidence synthesis on appointment reminder systems (TURNUP), which combined a conventional systematic review with theory development and a realist-informed review to explain why reminders worked inconsistently across settings — the answer turned out to depend on the reminder-patient interaction, the reminder’s accessibility, the healthcare setting, wider social conditions, and how cancellations and rebookings were actually handled (McLean et al., 2014). The other was a 2014–2015 evaluation of a demonstration trial that co-located public-sector nurse practitioners and specialist nurses into general practices, using a logic model and realist principles to examine drivers, contexts, mechanisms and outcomes. In both cases, the finding that mattered wasn’t whether the intervention “worked” — it was that the same activity behaved completely differently depending on context, and that the enabling conditions had to be understood before the activity could be judged at all. That is essentially PEASI’s Enablers step, arrived at through realist evaluation rather than named as such at the time.
Theory of Change: making the if-then explicit
Theory of Change emerged from a similar instinct, most influentially through Carol Weiss’s argument that comprehensive community initiatives kept failing to show measurable impact not because the initiatives were wrong, but because the theory connecting activities to long-term outcomes was never made explicit enough to test (Weiss, 1995). A Theory of Change forces an “if we do this, and if this holds, then we expect that” statement — and, crucially, asks what has to be true along the way for the final outcome to be plausible. It has become close to a required artefact in philanthropy and international development for exactly this reason: funders want to see the causal reasoning, not just the activity list.
Two recent projects used Theory of Change as their primary scaffolding, and both surfaced the same insight from very different starting points. One examined how to introduce interprofessional education across a large, disciplinarily diverse health faculty; the other examined how to scale inclusive postsecondary education for people with intellectual and developmental disability. Neither problem was best described by its presenting symptom — “we need more IPE” or “there isn’t enough access.” In both cases, structured problem definition revealed that the barrier wasn’t a lack of activity, policy or goodwill. It was a lack of alignment between individual, organisational and system-level conditions that were each, individually, in reasonable shape. That distinction — between a problem that looks like it needs an activity and a problem that actually needs coordination across levels — is difficult to see without an explicit theory of change, and it is precisely what the IOG lens in PEASI was built to surface systematically, every time.
And then there’s the rest of the alphabet
Programme logic, realist evaluation and Theory of Change are the three closest ancestors, but they are far from the only serious attempt to solve this problem. PRECEDE-PROCEED (Green & Kreuter) builds a comparably rigorous planning-and-evaluation model for health promotion. RE-AIM (Glasgow, Vogt & Boles, 1999) asks a public-health researcher to assess an intervention’s Reach, Effectiveness, Adoption, Implementation and Maintenance. CFIR, the Consolidated Framework for Implementation Research (Damschroder et al., 2009), gives implementation scientists a shared vocabulary for the domains that predict whether an intervention actually gets adopted in practice. Each of these is genuinely useful. Each was built by serious people solving a real problem. And each asks a practitioner to learn a new acronym, a new set of domains, and often a companion methodology, before they can use it on a Tuesday afternoon with a room full of stakeholders who have forty minutes and no evaluation training.
I have used variants of most of these frameworks across nearly three decades of health and workforce research, and I could not reliably reproduce most of their acronyms from memory without looking them up. That is not a failure of memory. It is a design flaw in how this field communicates its own best ideas.
What they all have in common
Strip away the vocabulary, and a set of recurring disciplines emerges across these traditions, although no single framework contains all of them. They encourage us to be explicit about what we are trying to change; to distinguish activities from the results they are intended to produce; to surface assumptions about how change is expected to happen; and to pay attention to the context in which an intervention is introduced. The more sophisticated approaches also remind us that relevant conditions may sit at several levels — individual, organisational and global (system) — and that an otherwise sensible intervention can fail when action is directed at the wrong part of the problem.
Where PEASI fits
PEASI is not a rejection of this literature. It keeps the causal chain that makes programme logic diagnostic rather than just descriptive. It keeps the insistence, from realist evaluation, that context determines whether an intervention works and that “does it work” is the wrong question until you’ve asked for whom and under what circumstances. It keeps the explicit if-then reasoning of Theory of Change, including the discipline of naming the intended impact before choosing the activity meant to produce it. And it keeps the multi-level analysis — individual, organisational, global, because it that turned out, across every one of these projects, one of the most persistent lessons across these projects was the need to look beyond a single level of the system.
What it deliberately drops is the vocabulary tax. Five ordinary English words — Problem, Enablers, Activities, Success, Impact — carrying the same underlying discipline as the methods above, without requiring a research team, a specialist qualification, or a six-month commissioning process before a team can use it on the problem sitting in front of them this week. That simplicity was not the easy option. It came after twenty years of applying the harder versions and paying close attention to which parts of the discipline were actually load-bearing, and which parts were just the accumulated jargon of a field that has never quite solved its own last-mile problem: not whether these methods work, but whether anyone outside a research unit can remember how to use them.
PEASI™ is a practical method for making sense of complex problems and developing workable pathways for change. You can explore the method and download the free PEASI Canvas at healthworxfutures.com/peasi.
References
Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science, 4, 50.
Glasgow, R. E., Vogt, T. M., & Boles, S. M. (1999). Evaluating the public health impact of health promotion interventions: the RE-AIM framework. American Journal of Public Health, 89(9), 1322–1327.
Green, L. W., & Kreuter, M. W. (2005). Health Program Planning: An Educational and Ecological Approach (4th ed.). McGraw-Hill.
W. K. Kellogg Foundation. (2004). Logic Model Development Guide. Battle Creek, MI: W.K. Kellogg Foundation.
McLean, S., Gee, M., Booth, A., Salway, S., Nancarrow, S., Cobb, M., & Bhanbhro, S. (2014). Targeting the Use of Reminders and Notifications for Uptake by Populations (TURNUP): a systematic review and evidence synthesis. Health Services and Delivery Research, 2(34). doi:10.3310/hsdr02340
Nancarrow, S. A., Roots, A., Grace, S., Moran, A. M., & Vanniekerk-Lyons, K. (2013). Implementing large scale workforce change: learning from 55 pilot sites of allied health workforce redesign in Queensland, Australia. Human Resources for Health, 11, 66.
Nilsen, P. (2015). Making sense of implementation theories, models and frameworks. Implementation Science, 10, 53.
Pawson, R., & Tilley, N. (1997). Realistic Evaluation. London: Sage.
Weiss, C. H. (1995). Nothing as practical as good theory: exploring theory-based evaluation for comprehensive community initiatives for children and families. In J. Connell, A. C. Kubisch, L. B. Schorr, & C. H. Weiss (Eds.), New Approaches to Evaluating Community Initiatives: Concepts, Methods, and Contexts (pp. 65–92). Aspen Institute.
