Community mental health researchers need practical ways to synthesize how interventions were delivered and experienced. The primary keyword for this guide is qualitative analysis of process evaluations, because researchers ask how to extract themes, compare frameworks, and preserve community voice. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. According to the PLOS One scoping review (Mere et al., 2026), process evaluation practice shifted markedly between 2006 and 2025, creating both an opportunity and a challenge for qualitative synthesis.
Key Takeaways
According to the PLOS One scoping review (Mere et al., 2026), 83 process evaluations of community mental health interventions published between 2006 and 2025 used 54 distinct primary frameworks, revealing both consolidation and fragmentation in framework use. The PLOS One review documents a temporal shift: implementation science frameworks rose to dominance after 2015 while participatory frameworks that foregrounded community co‑evaluation largely disappeared as primary frameworks after 2015.
- 83 studies met inclusion criteria in the review covering January 2006 to April 2025, reported in PLOS One on July 28, 2026.
- 54 distinct primary frameworks appeared across those 83 studies, with implementation science frameworks accounting for 39.8% (n = 33) and participatory approaches only 6.0% (n = 5) as primary frameworks, according to PLOS One (Mere et al., 2026).
- Geographic concentration was strong: North America accounted for 54.2% of studies and high‑income countries 83.1%, raising concerns about global generalizability (PLOS One, 2026).
What happened and how the scoping review measured it
The PLOS One scoping review directly mapped which theoretical frameworks guided process evaluations in community mental health between January 2006 and April 2025.
According to PLOS One (Mere et al., 2026), the authors searched PubMed, Web of Science, and EBSCOhost, screened 1, 143 records, and included 83 studies after full‑text review using PRISMA‑ScR methods.
The review used inductive coding to group 54 named frameworks into 14 categories and then reported frequencies, time trends (2006–2015, 2016–2020, 2021–2025), and geographic patterns to characterize shifts from participatory to implementation science approaches.
Findings snapshot
| Date / Period | Metric | Value (as reported) | Implication |
|---|---|---|---|
| January 2006–April 2025 | Included studies | 83 studies | Scope shows nearly two decades of literature mapped (PLOS One, 2026) |
| 2006–2025 | Distinct primary frameworks | 54 frameworks | High framework diversity despite some categorical consolidation (PLOS One, 2026) |
| 2006–2025 | Implementation science share | 39.8% (n = 33) | Implementation science became the dominant category after 2015 (PLOS One, 2026) |
| 2006–2015 vs post‑2015 | Participatory primary frameworks | 23.8% early, 0% after 2015 (primary use); n = 5 total | Participatory frameworks receded as primary organizing frameworks after 2015 (PLOS One, 2026) |
| As of April 2025 | Geographic concentration | North America 54.2%; High‑income 83.1% | Findings may not generalize to LMIC contexts (PLOS One, 2026) |
Implications for qualitative researchers and evaluators
Researchers designing qualitative analyses for process evaluations should match framework choice to evaluation goals and be explicit about tradeoffs.
According to PLOS One (Mere et al., 2026), determinant frameworks like CFIR suit questions about barriers and facilitators while evaluation frameworks like RE‑AIM suit reach and sustainability questions; participatory frameworks prioritize partnership, empowerment, and community‑led interpretation.
Because the PLOS One review found categorical consolidation around implementation science yet fragmentation across individual frameworks, qualitative teams should document why they selected specific frameworks and consider using secondary frameworks to fill conceptual gaps, as many studies in the review did.
How Evidano helps with qualitative analysis of process evaluations
Problem: Unwieldy, fragmented frameworks make synthesis slow
Solution: Evidano speeds cross‑study thematic synthesis by ingesting transcripts, study reports, and framework codebooks and producing harmonized thematic maps and cross‑segment analyses.
Evidano integrates document ingestion, AI chat over your documents, and visualization tools so teams can compare which CFIR or RE‑AIM constructs appear across studies and where community‑centered codes are absent or under‑represented.
Problem: Preserving community voice while using systematized frameworks
Solution: Evidano supports layered coding workflows so researchers can tag community co‑analysis outputs separately, merge researcher and community codes, and quantify where participatory themes appear as primary or secondary constructs.
Evidano’s thematic and cross‑segment analysis features help you measure how often community‑sourced codes occur and how they map onto implementation science constructs.
Problem: Manual transcription and multi‑language datasets slow synthesis
Solution: Evidano provides automated transcription and translation with customizable dictionaries and PII redaction to prepare interviews and community advisory notes for analysis quickly and securely.
See the Evidano speech‑to‑text feature for transcription and the features page for an overview of thematic and visual analytics.
FAQ: qualitative analysis of process evaluations
What did the PLOS One review find about the disappearance of participatory primary frameworks after 2015?
Answer: The PLOS One review found that participatory frameworks were used as primary organizing frameworks in five studies published between 2006 and 2015 and were not used as primary frameworks after 2015.
According to PLOS One (Mere et al., 2026), participatory approaches continued to appear as secondary frameworks in eight later studies, suggesting a shift from primary organizing role to supplementary role rather than complete abandonment.
Which frameworks appeared most often across process evaluations in the review?
Answer: RE‑AIM, CFIR, and the MRC Framework were the most frequently used individual frameworks, but together they accounted for only 27.7% of studies.
The PLOS One review reports RE‑AIM in 9 studies, CFIR in 8 studies, and MRC in 6 studies between 2006 and April 2025 (Mere et al., 2026), indicating fragmentation at the framework level despite categorical consolidation around implementation science.
How should qualitative teams choose a framework for a community mental health process evaluation?
Answer: Choose the framework that aligns with your primary evaluation question, and document why secondary frameworks are needed.
According to PLOS One (Mere et al., 2026), determinant frameworks fit questions about implementation barriers, evaluation frameworks fit reach/adoption questions, and participatory frameworks are appropriate when community partnership and co‑interpretation are primary aims.
Can AI help preserve community perspectives in analysis without losing methodological rigor?
Answer: Yes, when AI tools are used to accelerate coding and synthesis while keeping community codes distinct and auditable.
Evidano supports workflows where community‑generated codes and researcher codes are both retained and compared, enabling transparent quantification of how participatory themes map onto implementation science constructs.
Conclusion & Next Steps
The PLOS One scoping review (Mere et al., 2026) documents a shift from "From participation to systematization" and calls for approaches that combine rigor with meaningful stakeholder partnership: in the authors' words, moving toward "systematized participation."
For qualitative researchers, the practical next step is to select frameworks keyed to evaluation questions, document secondary frameworks, and preserve community codes during synthesis.
To accelerate that work, use AI‑assisted tools that ingest transcripts, harmonize codes, and produce auditable thematic maps; try Evidano’s thematic and transcription features to shorten manual work and keep community voice explicit.
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