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 published July 28, 2026, process evaluation frameworks in community mental health shifted from participatory traditions toward implementation science between 2006 and 2025. The primary keyword for this post is process evaluation frameworks, and this article summarizes the reviews quantitative findings, explains what changed and why, and shows how AI-enabled qualitative research tools speed framework-aligned synthesis for evaluation teams.
Key Takeaways
According to the PLOS One scoping review published July 28, 2026, the field moved from community-participatory frameworks to systematized implementation science frameworks between 2006 and 2025 (PLOS One).
- The review searched 1, 143 records and included 83 studies published between 2006 and April 2025, as reported in PLOS One on July 28, 2026.
- PLOS One found 54 distinct primary frameworks organized into 14 categories, with implementation science frameworks accounting for 39.8% overall (n = 33) in the 83-study sample.
- PLOS One reports participatory approaches made up 23.8% of early studies in 2006015 but were absent as primary frameworks after 2015, while implementation science rose to over 50% in 2016020.
- Geographic concentration was heavy: PLOS One reports North America contributed 54.2% of studies and high-income countries contributed 83.1% of studies, raising equity concerns.
What happened: how the PLOS One review measured change
Answer: The PLOS One scoping review mapped framework usage in process evaluations of community mental health interventions from 2006 to April 2025 and quantified temporal and geographic trends.
The PLOS One team searched PubMed, Web of Science, and EBSCOhost and identified 1, 143 records, from which 83 studies met inclusion criteria, as described in the Methods section of the PLOS One article published July 28, 2026.
The PLOS One review recorded 54 distinct primary frameworks and inductively organized them into 14 categories; implementation science frameworks were the largest category at 39.8% (n = 33), with CFIR and RE-AIM among the most frequent individual frameworks.
The PLOS One review grouped the time series into three periods: 2006015, 2016020, and 2021025; participatory frameworks clustered in 2006015 (23.8%) and were not used as primary frameworks after 2015, while implementation science frameworks rose from 14.3% to 52.0% in 2016020, according to PLOS One.
Findings snapshot
| Date or Period | Metric | Value (PLOS One) | Implication |
|---|---|---|---|
| Search period (2006025) | Records identified | 1, 143 | Large initial literature captured; review is broad |
| Inclusion (published through Apr 2025) | Studies included | 83 | Empirical base for typology and trend analysis |
| Overall | Distinct primary frameworks | 54 (across 83 studies) | High fragmentation at framework level |
| Overall | Framework categories | 14 | Typology facilitates selection and mapping |
| 2006015 vs after 2015 | Participatory approaches as primary framework | 23.8% in 2006015; 0% after 2015 | Repositioning of community participation |
| 2016020 | Implementation science frameworks | 52.0% of studies | Systematization and consolidation |
| Geography (overall) | North America share | 54.2% | Potential Western bias in framework development |
| Geography (overall) | High-income country share | 83.1% | Equity and generalizability concerns |
Implications for qualitative researchers and evaluation teams
Answer: Researchers should match framework choice to evaluation questions and explicitly document why one framework was selected over others, because the PLOS One review shows framework choice structures what is observed and reported.
PLOS One recommends that researchers justify framework selection and consider mixing frameworks when single frameworks omit key dimensions; the review found 73% of studies (n = 61) referenced at least one secondary framework, indicating common hybrid use.
PLOS One highlights equity implications: with 54.2% of studies from North America and 83.1% from high-income countries, external validity for low- and middle-income contexts is limited and adaptation or alternative frameworks may be necessary.
PLOS One notes that consolidation around implementation science increases comparability but risks sidelining community authority and partnership when participatory frameworks are not used as organizing structures.
How Evidano helps: bridge systematization and participation with AI
Problem: Fragmented frameworks slow synthesis
Answer: Fragmentation across 54 frameworks in 83 studies makes cross-study synthesis slow and error prone, as reported by PLOS One.
Solution: Evidano automates thematic, content, frequency, and cross-segment analyses so teams can compare constructs across studies without coding each dataset from scratch.
Problem: Process evaluations need rapid, transparent coding
Answer: PLOS One shows many studies reference multiple frameworks and hybridize approaches, increasing coding complexity.
Solution: Evidanos AI-assisted coding supports hierarchical codes and subcodes, enabling you to map CFIR domains, RE-AIM constructs, and participatory codes side by side and export reproducible codebooks.
Problem: Transcription and multilingual data bottlenecks
Answer: Community evaluations often collect audio and multilingual interviews that require accurate transcription and redaction.
Solution: Evidano is an AI-powered qualitative data analysis platform that ingests transcripts and offers speech-to-text with custom dictionaries and PII redaction, plus translation tools for cross-language analyses.
Problem: Stakeholder engagement and reporting consistency
Answer: PLOS One documents a move from participatory frameworks to researcher-led systematization, which can reduce community influence on evaluation framing.
Solution: Evidano supports collaborative workspaces and exportable visualizations (co-occurrence networks, hierarchical code trees) so community partners can review coding frames and interpretation, helping operationalize "systematized participation".
Try it with your data
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Learn more about features at the Evidano features page and see how automated synthesis reduces turnaround time for process evaluations.
FAQ: process evaluation frameworks
What is a process evaluation framework and why does it matter?
Answer: A process evaluation framework is a structured set of concepts that guides what implementation aspects are measured and how, and PLOS One shows framework choice shapes data collection and interpretation.
The PLOS One review categorized frameworks into 14 types and found that framework choice determines focus areas such as fidelity, reach, adaptation, or community partnership, which affects what conclusions an evaluation can support.
How did framework use change between 2006 and 2025?
Answer: Framework use shifted from participatory approaches in 2006015 to implementation science dominance in later periods, according to PLOS One.
Specifically, PLOS One reports participatory approaches accounted for 23.8% of studies in 2006015 and were absent as primary frameworks after 2015, while implementation science frameworks rose from 14.3% to 52.0% in 2016020.
Should I use a single framework or combine frameworks for my community mental health study?
Answer: Use the framework that best answers your primary evaluation question and consider explicit secondary frameworks when needed, a practice common in the PLOS One sample.
PLOS One found 73% of studies referenced secondary frameworks, suggesting hybrid approaches help cover gaps where single frameworks lack breadth.
How can AI tools speed process evaluation synthesis without losing community voice?
Answer: AI accelerates coding and triangulation while leaving decisions about codes and interpretation to researchers and community partners.
Using platforms like Evidano, teams can rapidly generate thematic and cross-segment analyses and then share interactive outputs with partners for validation, making participation practical within systematized evaluations.
Does the PLOS One review recommend a single best framework?
Answer: No, PLOS One does not endorse a single best framework; it offers a typology and recommends matching frameworks to evaluation goals.
PLOS One authors argue that framework selection should be justified and that future guidance should help researchers choose frameworks based on evaluation purpose and context.
Conclusion & Next Steps
The PLOS One scoping review published July 28, 2026 documents a move from participation to systematization in community mental health process evaluation, quantifying 54 frameworks across 83 studies and offering a 14-category typology to guide framework selection.
Researchers should match frameworks to evaluation questions, transparently report primary and secondary frameworks, and prioritize equity when applying frameworks developed in high-income settings, as highlighted by PLOS One.
Evidano helps teams reconcile systematization and community partnership by automating coding, enabling collaborative review, and supporting accurate transcription and translation through speech-to-text tools.
To test these workflows on your data, Try Evidano for free.