This post explains how qualitative researchers can extract actionable insights from a new realist review protocol on adolescent-led interventions against the fossil fuel industry, using AI-enabled methods. The primary keyword for this guide is "qualitative analysis of youth climate interventions" and the audience is qualitative researchers, public health teams, and policy analysts who must synthesise complex mixed sources (academic papers, grey literature, organisational reports). The PLOS One protocol (published 13 August 2026) maps a realist approach using Context–Mechanism–Outcome configurations and extensive stakeholder input; this post shows which protocol details matter for qualitative synthesis and how AI tools can speed transparent, reproducible analysis.
Key Takeaways
The PLOS One protocol (published 13 August 2026) proposes a realist review to identify how adolescent-involving interventions can counter fossil fuel industry practices and reduce health inequities: PLOS One.
- 1) The protocol states an estimated 2.5 million deaths annually are attributable to outdoor air pollution from burning fossil fuels, cited in the PLOS One introduction (Deivanayagam et al., PLOS One, 2026).
- 2) The protocol reports that 67% of greenhouse gases are attributable to fossil fuel combustion, quoted in the PLOS One introduction (Deivanayagam et al., PLOS One, 2026).
- 3) The PLOS One study began October 2024, aimed to complete evidence searches and selection by April 2026, and expected to report results in July 2026 (Deivanayagam et al., PLOS One, published 13 August 2026).
- 4) The review uses RAMESES standards and Context–Mechanism–Outcome configurations to produce transferable programme theories for interventions involving adolescents (Deivanayagam et al., PLOS One, 2026).
What happened and how the PLOS One protocol works
The protocol defines a realist review that seeks to explain how, why, and for whom adolescent-involving interventions counter the fossil fuel industry to address health inequities, as specified in Deivanayagam et al., PLOS One (2026).
Deivanayagam et al., PLOS One (2026) set out five realist stages: (1) scope definition, (2) build initial programme theory, (3) systematic evidence search, (4) selection and appraisal, and (5) extraction and synthesis, using RAMESES standards for realist syntheses.
The protocol operationalises adolescents as 10–19-year-olds, uses Hart’s ladder to classify levels of participation, and adopts a structural racism lens to prioritise equity-relevant evidence (Deivanayagam et al., PLOS One, 2026).
The protocol explicitly includes grey literature, policy documents, and organisational reports in searches across PubMed, Scopus, GEOBASE, and hand searches of organisations such as UNICEF and WHO to capture movement ecosystem evidence (Deivanayagam et al., PLOS One, 2026).
Examples of direct phrasing from the protocol include: "The fossil fuel industry’s practices and products create wide‑ranging harms to human health" and the definition of corporate political activity as "practices to secure preferential treatment and/or prevent, shape, circumvent or undermine public policies" (Deivanayagam et al., PLOS One, 2026).
Findings snapshot (protocol numbers and timeline)
| Date / Item | Metric | Value | Implication for qualitative synthesis |
|---|---|---|---|
| 13 August 2026 | Protocol publication | Deivanayagam et al., PLOS One (2026) | Use the protocol as a registered plan and source of CMO definitions |
| October 2024 | Study start date | Project began | Track changes to IPTs over time during extraction |
| April 2026 | Data collection target | Evidence searches and selection aimed complete by April 2026 | Expect a large grey literature set; plan for deduplication and provenance tagging |
| July 2026 | Results expected | Realist review results expected July 2026 | Prepare synthesis strategy to integrate interim stakeholder feedback |
| Ongoing | Youth advisory representation | YAB of six adolescents aged 14–17 involved during protocol design | Document YAB inputs as context for mechanisms in CMO extraction |
Implications for qualitative researchers and public health teams
Qualitative researchers must prioritise context and mechanism coding because the protocol is designed to produce transferable theories rather than single-study effect estimates (Deivanayagam et al., PLOS One, 2026).
Researchers should extract detailed contextual fields described in the protocol: geographic setting, participant demographics, degree of adolescent participation (Hart’s ladder), intervention activities, and how structural racism is considered (Deivanayagam et al., PLOS One, 2026).
Public health teams should note the protocol recommends inclusion of movement ecosystem documents and corporate political activity analyses, so synthesis must capture narrative strategies and power relations in addition to outcomes (Deivanayagam et al., PLOS One, 2026).
For mixed evidence sets, the protocol advises appraisal by relevance, richness, and rigour rather than by hierarchical study design alone; qualitative teams should therefore keep iterative memos explaining why sources inform or refute programme theories (Deivanayagam et al., PLOS One, 2026).
How Evidano helps (problem → solution mappings)
Problem: Large, mixed-source evidence sets are slow to synthesise
Solution: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano ingests PDFs, web-scraped reports, and transcripts to auto-extract contexts, actors, and activities mentioned in the PLOS One protocol, speeding the stage 3 systematic evidence search and stage 5 extraction and synthesis.
Evidano's thematic clustering and co-occurrence visualisations let teams map Context–Mechanism–Outcome patterns across academic and grey literature in minutes rather than weeks; see the platform features at Evidano features.
Problem: Preserving provenance and stakeholder inputs (YAB, ESG)
Solution: Evidano tags source provenance and supports collaborative annotations so Youth Advisory Board inputs and Expert Steering Group reflections are preserved alongside extracted CMO notes.
This provenance tracking aligns with the protocol’s emphasis on stakeholder-informed IPT refinement (Deivanayagam et al., PLOS One, 2026).
Problem: Reproducible coding of equity and structural racism indicators
Solution: Evidano supports custom codebooks and hierarchical codes, enabling teams to operationalise the protocol’s structural racism lens across studies and reports and to produce cross-segment frequency analyses by age, race, and participation level.
Problem: Converting synthesis into visual CMO summaries
Solution: Evidano generates exportable visual summaries (code trees, co-occurrence networks) that match the protocol’s plan to create visual CMO configurations for validation with advisory groups.
FAQ: qualitative analysis of youth climate interventions
What is a realist review and why does it matter for qualitative analysis?
Answer: A realist review explains how and why interventions work by developing Context–Mechanism–Outcome configurations.
The PLOS One protocol follows RAMESES standards and uses CMO configurations to move from initial programme theories to refined, transferable explanations (Deivanayagam et al., PLOS One, 2026).
How can AI speed thematic synthesis for mixed academic and grey evidence?
Answer: AI accelerates initial coding, cluster detection, and extraction of contextual fields across hundreds of documents.
Deivanayagam et al., PLOS One (2026) instructs inclusion of diverse sources; AI tools can quickly surface recurring mechanisms and policy framings that warrant deeper manual interpretation.
Which protocol statistics should researchers log when preparing a synthesis?
Answer: Log absolute dates, sample numbers, population ages, participation levels, and timeline milestones.
The PLOS One protocol provides key numbers to track: 2.5 million annual deaths from outdoor air pollution and 67% of greenhouse gases linked to fossil fuel combustion, and project timeline dates such as study start October 2024 and expected results July 2026 (Deivanayagam et al., PLOS One, 2026).
How should teams handle equity and structural racism when coding evidence?
Answer: Use a pre-specified equity codebook and prioritise sources that explicitly address racism while still noting implicit measures of disparity.
Deivanayagam et al., PLOS One (2026) recommends ranking studies that reference health inequities linked to racism higher during appraisal and coding both explicit and implicit indicators during extraction.
How does Evidano protect sensitive data during qualitative synthesis?
Answer: Evidano encrypts uploaded data and offers access controls suitable for collaborative teams.
For details on security and data governance see Evidano data security.
Conclusion & Next Steps
The PLOS One realist review protocol (Deivanayagam et al., PLOS One, published 13 August 2026) sets a reproducible plan for understanding how adolescent-involving interventions can challenge fossil fuel industry practices to reduce health inequities.
Qualitative teams should prioritise context-rich extraction, stakeholder provenance, and equity-centred coding as the protocol prescribes, and can use AI-enabled platforms to speed CMO building and visual validation.
If your team needs a secure, collaborative platform for thematic, frequency, and cross-segment analyses that maps directly to realist CMO work, consider an AI-assisted workflow.
Try Evidano for free at Try Evidano for free.
Topics
- qualitative analysis of youth climate interventions
- realist review youth interventions
- AI qualitative research
- youth participatory research fossil fuels
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