Evidence for Impact helps researchers, policymakers and practitioners turn sprawling bodies of research into trustworthy, actionable knowledge — pairing rigorous systematic review methodology with responsibly designed AI assistance.
One question. Transparent methods. Evidence a decision can rest on.
Good decisions rarely fail for lack of research. They fail because the research is scattered, contradictory, uneven in quality — and impossible to absorb one study at a time.
Any individual study can be underpowered, context-bound or simply wrong. Basing policy on one striking finding invites costly reversals. Synthesis weighs the whole body of evidence, not the most quotable slice of it.
Educators, clinicians and policymakers cannot keep up with millions of new papers a year. Without systematic synthesis, decisions default to habit, anecdote or whichever evidence happens to be nearby.
Effective interventions routinely take years — often estimated at more than a decade — to reach the classrooms, clinics and communities they were designed for. Synthesis and implementation science exist to close that gap.
Evidence-informed policy and practice rests on a simple premise: when public money, professional time and human wellbeing are at stake, decisions should be anchored in the best available research — appraised honestly, including its limitations and uncertainty.
That anchoring work is what knowledge synthesis does. A well-conducted review defines a precise question, searches exhaustively, screens against pre-specified criteria, appraises study quality, and integrates findings using transparent, reproducible methods. The result is not just a summary — it is an audit trail from question to conclusion that others can scrutinise and replicate.
And synthesis is only half the journey. Knowledge implementation — translating what reviews find into guidelines, programs, curricula and behaviour — determines whether evidence ever changes anything. Evidence for Impact works across both halves.
“Science is cumulative, and scientists should cumulate scientifically.” — Iain Chalmers & colleagues, on the rationale for systematic reviews
A systematic review is defined less by what it finds than by how it is planned. The protocol — written and ideally registered before searching starts — commits the team to its methods in advance, protecting the review from hindsight bias and selective reporting.
Frame the question with a structured template — PICO for intervention effects, PCC for scoping questions, SPIDER for qualitative evidence. A precise question determines everything downstream: eligibility criteria, search terms, and what “an answer” will look like.
Document eligibility criteria, planned search sources, screening procedures, appraisal tools and synthesis methods — then register the protocol so deviations are visible and justified rather than silent.
Search multiple databases with a strategy built around controlled vocabulary and keywords, supplemented by citation snowballing, registries and grey literature. Every string, source and date is recorded so the search can be reproduced.
Titles and abstracts first, then full texts — each record assessed against the pre-specified criteria, ideally by two independent reviewers with conflicts resolved by discussion. Exclusion reasons at full text are logged for the PRISMA flow diagram.
Included studies are appraised with validated, published instruments matched to their designs — never ad-hoc checklists. Appraisal shapes how much weight each study carries in the synthesis and how confident the conclusions can be.
A piloted extraction form captures the same fields from every study — populations, contexts, interventions, measures, outcomes — creating the structured dataset the synthesis is built on, with extraction verified for accuracy.
Where studies are similar enough, effects are pooled statistically through meta-analysis; where they are not, structured narrative, thematic or framework synthesis is used — with heterogeneity explored rather than hidden.
Findings are reported against PRISMA 2020, with the flow diagram, search strings and data available for scrutiny. Then the real work begins: briefs, guidelines and tools that carry the evidence into policy and practice.
“Systematic review” names a family, not a single method. Choosing the right member of the family — matched to the question, the evidence base and the decision timeline — is itself a methodological decision.
The reference standard: exhaustive search, pre-specified criteria, quality appraisal and transparent synthesis to answer a focused question.
Best for: “Does intervention X work, for whom, and under what conditions?”
Statistical pooling of comparable studies within a systematic review, producing a weighted overall effect estimate and exploring heterogeneity.
Best for: quantifying how large and how consistent an effect is across studies.
Maps the extent, range and nature of evidence on a broad topic, identifying concepts, gaps and research clusters rather than pooling effects.
Best for: “What do we know so far — and where are the gaps?”
A streamlined systematic review that transparently abbreviates steps — fewer databases, single screening — to answer urgent policy questions in weeks, not years.
Best for: decision-makers who need defensible evidence on a deadline.
A review of reviews: synthesises existing systematic reviews and meta-analyses to give the highest-level overview of a mature evidence base.
Best for: fields where many reviews already exist and need reconciling.
An expert-led, interpretive account of a literature. Valuable for theory-building and orientation, but without systematic methods it is vulnerable to selection bias.
Best for: conceptual framing, theoretical argument and teaching.
Integrates quantitative and qualitative evidence — convergent or sequential — so reviews can answer both “does it work?” and “how, and for whom?”
Best for: complex interventions where numbers and experiences both matter.
A review maintained as a continually updated service, re-run as new studies appear — increasingly feasible as AI lowers the cost of re-screening.
Best for: fast-moving fields where last year’s answer is already stale.
Evidence4impact.org is dedicated to leveraging artificial intelligence to assist the knowledge synthesis and implementation process — building open, practical tools that compress the mechanical work of reviewing so human judgement can go where it is irreplaceable.
Large language models triage titles and abstracts against pre-specified eligibility criteria — including cascade designs that reserve deeper model reads for borderline records — cutting screening burden dramatically for low-prevalence corpora while keeping reviewers in control of every inclusion decision.
In an era of fabricated and “hallucinated” citations, our verification tools cross-check reference lists against DOI registries and open scholarly indexes such as Crossref and OpenAlex — confirming that every cited study actually exists and says what it is claimed to say.
Structured AI assistance for data extraction and critical appraisal — surfacing candidate values with the evidence passage attached, so reviewers verify against the source rather than transcribe by hand, and validated appraisal instruments stay at the centre.
Evidence only matters when it moves. We build tools and workflows for the translation side — journal and integrity profiling, plain-language outputs, and decision-support prototypes that carry review findings into policy, education and practice settings.
Every tool published under Evidence for Impact follows the same principles: AI proposes, humans decide; methods and prompts are documented so results are auditable; named instruments and measures come from published, validated sources; and limitations are reported as candidly as capabilities. Rigour is the product — AI is the accelerant.
Free, browser-based tools for the working stages of evidence synthesis — new tools are added as they're built and tested.
Paste a reference list and cross-check every citation against Crossref and OpenAlex — flagging entries that don't exist, don't match, or look fabricated.
Try it nowTurn a list of DOIs into complete, correctly formatted APA 7 references in seconds, pulled directly from registry metadata.
Try it nowProfile any journal using open OpenAlex data — publication volume, citation patterns and integrity signals — before you submit or cite.
Open toolDr Cong Ngo is an evidence synthesis specialist with extensive experience conducting systematic, scoping and rapid reviews for research, policy and practice audiences.
His work centres on evidence synthesis methodology and knowledge translation — from protocol design and comprehensive searching through critical appraisal, meta-analysis and implementation. Evidence for Impact brings that methodological expertise together with hands-on tool development: rigorous reviews, and AI-assisted tools that make rigorous methods faster and more accessible to the research community.
Explore the growing suite of Evidence for Impact tools, or get in touch about collaboration on reviews, methods and knowledge translation.
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