Qualitative analysis of child poverty in African conflict zones reveals non-monetary, conflict-specific deprivations missing from standard indices, and a PLoS One scoping review protocol (published June 29, 2026) maps those multidimensional deprivations using rigorous JBI-based methods.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that accelerates reproducible synthesis of multidimensional child poverty evidence in African conflict zones.
The PLoS One scoping review protocol (published June 29, 2026) commits to mapping multidimensional child poverty in African armed conflicts and to developing a conflict-sensitive framework that highlights gaps in existing measures.
- The protocol was published on June 29, 2026 and registers related materials on OSF.
- The protocol includes English, French, and Portuguese sources and incorporates grey literature such as NGO and UN reports.
- Operationalizing the protocol benefits from scalable screening, machine translation verified by bilingual reviewers, and AI-assisted coding to produce audit-ready thematic maps.
Fast take + source
Fast take: A scoping review protocol commits to map multidimensional child poverty in African armed conflicts and to develop a conflict-sensitive framework to capture underrepresented deprivations.
What happened: A scoping review protocol (Raja et al.) published in PLOS One on June 29, 2026 commits to map multidimensional child poverty in African armed conflicts and to develop a conflict-sensitive framework for child deprivation.
Why it matters: The protocol highlights gaps in existing measures (MPI, MODA) and will synthesize evidence across languages and grey literature to identify underrepresented dimensions such as safety, legal status, psychosocial well-being, and mobility.
Findings snapshot
| Item | Value | Source / note |
|---|---|---|
| Published | June 29, 2026 | PLOS One protocol |
| Received / Accepted | Received: Jan 6, 2026 · Accepted: Jun 14, 2026 | PLOS metadata |
| Children in conflict-lethal environments (global) | ≈ 246 million | Protocol cites global estimates |
| Africa: active NIACs | More than 35 | Protocol (cites Geneva Academy, 2024) |
| Child poverty in fragile vs non-fragile states | Fragile 38.8% · Non-fragile 10.1% | Protocol references comparative rates |
| Prevalence in sub-Saharan conflict settings | Up to 6 in 10 children | Protocol literature synthesis |
| Languages included | English, French, Portuguese | Protocol search criteria |
| Expected timeline | Screening & extraction in 2026; results expected 2027 | Protocol methods |
What the protocol does (methods in plain English)
The protocol follows Joanna Briggs Institute scoping review methods, with broad searches, multilingual sources, grey literature inclusion, dual screening, and iterative data extraction to map dimensions to existing indices and conflict-specific domains.
The authors follow Joanna Briggs Institute (JBI) scoping review methods: broad search across health and social science databases, inclusion of quantitative, qualitative, mixed methods, dissertations and NGO reports, dual independent screening, and an iterative data-extraction tool.
Key operational choices that matter for qualitative researchers: inclusion of grey literature (UNICEF, WHO, UNDP), multilingual sources (French/Portuguese translated preliminarily with Google Translate), citation management via Zotero, screening with Covidence, and qualitative content analysis to develop a multidimensional framework.
- Inclusion: children birth–18 in African conflict contexts; any study that addresses ≥1 poverty dimension.
- Conflict definition: ≥25 battle-related deaths/year (per UCDP convention).
- Outputs: descriptive tables, thematic maps, and a proposed conflict-sensitive child poverty framework.
Ethics note: this is synthesis research using published and public documents, non-diagnostic and research-focused; follow consent and data-use norms when handling primary transcripts.
So what for researchers, policy teams, and NGOs?
For qualitative researchers
For qualitative researchers, expect heterogenous reporting and adopt iterative extraction tools and pilot testing as described in the protocol.
Expect heterogenous reporting: single-dimension studies (for example, undernutrition) alongside mixed-dimension qualitative work. The protocol’s iterative extraction tool and pilot testing (first 10 papers) are best practices you can copy.
Plan for translation verification: preliminary machine translation is acceptable for screening but validate key quotes with bilingual reviewers before quoting or coding.
For policy & evaluation teams
For policy and evaluation teams, a conflict-sensitive poverty framework helps target interventions beyond income, including legal status, psychosocial support, and child protection metrics.
A conflict-sensitive poverty framework will help target interventions beyond income, for example legal status, psychosocial support, and child protection metrics.
Use scoping results to prioritize data collection and indicators for program monitoring in conflict-affected districts.
For NGOs & funders
For NGOs and funders, grey literature and NGO reports are central, so invest in accessible, machine-readable reporting formats to increase evidence visibility.
Grey literature and NGO reports are central; the protocol explicitly includes these sources, so invest in accessible, machine-readable reporting formats to increase evidence visibility.
Expect recommendations to shift funding toward overlapping deprivations (nutrition + protection + documentation).
Do more, faster with Evidano
Ingest multilingual documents & transcripts
Evidano imports PDFs, Word documents, interview transcripts, NGO reports, and spreadsheets to match the protocol’s mixed-source search across databases and grey literature.
Evidano imports PDFs, Word docs, interview transcripts, NGO reports, and spreadsheets, matching the protocol’s mixed-source search (databases + grey literature).
Auto-translate (custom dictionary) and retain original text for audit trails, so you can screen French or Portuguese reports like the authors plan, but faster and with reproducible logs.
Automate thematic coding and mapping
Evidano can generate AI-assisted codebooks from seed terms and apply hierarchical codes across hundreds of documents to accelerate framework development.
Run AI-assisted codebook generation from seed terms (for example, safety, legal status, psychosocial) and apply hierarchical codes → subcodes across hundreds of documents.
Combine thematic frequency, co-occurrence networks, and quote extraction to build the multidimensional framework the protocol aims to produce.
Cross-segment comparisons & visual reports
Evidano supports cross-segment comparisons by country, conflict stage, age subgroup, or data type and exports stakeholder-ready visuals.
Compare by country, conflict stage (peri/during/post), age subgroup, or data type (qualitative vs quantitative) using cross-segment analyses and exportable visuals (word clouds, co-occurrence graphs).
Produce stakeholder-ready tables compatible with PRISMA and JBI reporting requirements.
Secure, reproducible, and audit-ready
Evidano encrypts data end-to-end and does not use customer data to train third-party models, aligning with ethical expectations for sensitive child-focused research.
Data encrypted end-to-end and never used to train third-party models, aligns with ethical expectations for sensitive child-focused research.
Export your extraction tool and coding decisions to share reproducible methods with reviewers and funders.
Quick example
A quick example: upload the protocol and 50 seed articles, auto-translate non-English texts, generate candidate themes, pilot-apply a codebook, refine codes, and run cross-segment prevalence to export an evidence map.
Upload the protocol and 50 seed articles → auto-translate non-English texts → generate candidate themes → pilot-apply codebook to 10 articles → refine codes → run cross-segment prevalence and export evidence map.
Two-week pilot checklist (runbook)
This two-week pilot checklist gives a pragmatic workflow to mirror the protocol and accelerate synthesis.
- Day 1–2: Aggregate sources (databases export + NGO PDFs + theses) and upload to Evidano.
- Day 3–4: Auto-translate French/Portuguese documents; flag high-priority texts for bilingual review.
- Day 5–7: Generate an initial codebook from seed terms; run AI-assisted coding on a 10–20 document pilot set.
- Day 8–10: Review coded excerpts, adjust code hierarchy, and lock the codebook.
- Day 11–12: Run cross-segment analyses (country, conflict stage, age) and produce co-occurrence networks.
- Day 13–14: Export PRISMA-style flow diagram, thematic tables, and a 2-page stakeholder brief.
Wrapping up & next steps
Raja et al.’s PLoS One protocol (published June 29, 2026) argues for a conflict-sensitive child poverty framework, and AI tools can make that mapping faster and more reproducible.
Raja et al.’s PLoS protocol (published June 29, 2026) makes a strong case for a conflict-sensitive child poverty framework; AI tools make that mapping faster and more reproducible.
For teams preparing the scoping review or planning primary qualitative work in African conflict settings, the practical gains are clear: scale screening and translation, reduce manual coding, and produce stakeholder-ready visuals while preserving ethics and data security.
Explore how Evidano can run the workflows above and produce audit-ready outputs.
Strong CTA: Try Evidano for free.
FAQ: Qualitative analysis of child poverty in conflict
What does the scoping review protocol aim to map and why is that important?
The scoping review protocol aims to map multidimensional child poverty in African armed conflicts to develop a conflict-sensitive framework that captures deprivations beyond income.
The protocol commits to mapping multidimensional child poverty and to developing a conflict-sensitive framework, because current indices miss dimensions such as safety, legal status, psychosocial well-being, and mobility.
Which languages and sources does the protocol include?
The protocol includes English, French, and Portuguese sources and explicitly includes grey literature such as NGO and UN reports.
The protocol’s search criteria include English, French, Portuguese and make space for grey literature (UNICEF, WHO, UNDP) along with academic databases and theses.
How does the protocol define conflict for included studies?
The protocol uses a conflict definition of at least 25 battle-related deaths per year, consistent with UCDP conventions.
The conflict definition in the protocol is ≥25 battle-related deaths/year, which guides inclusion of conflict-affected settings.
How can teams operationalize the protocol faster using AI tools?
Teams can operationalize the protocol faster by scaling screening, using machine translation with bilingual verification, and applying AI-assisted coding to generate reproducible thematic maps.
The post outlines a two-week pilot: aggregate sources, auto-translate, generate a codebook, pilot coding, refine the codebook, run cross-segment analyses, and export PRISMA-style diagrams and stakeholder briefs.