Qualitative process evaluation frameworks shape which questions researchers ask about how community mental health interventions are delivered. The primary keyword for this post is qualitative process evaluation frameworks. According to the PLOS One scoping review by Mere et al. (published July 28, 2026), 83 studies published between 2006 and 2025 used 54 distinct primary frameworks, revealing a shift in how evaluations are framed. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Key Takeaways
According to the PLOS One scoping review (Mere et al., 2026), the field shifted from community-rooted participatory frameworks to systematized implementation science frameworks across 2006–2025 (PLOS One).
- 83 studies met inclusion criteria in the scoping review, covering publications from 2006 to April 2025, as reported by Mere et al. in PLOS One on July 28, 2026.
- Mere et al. found 54 distinct primary frameworks across those 83 studies, with implementation science frameworks accounting for 39.8% of primary frameworks in the dataset.
- Participatory approaches appeared as the primary framework in five studies (6.0%), all published in 2006–2015, and were absent as primary frameworks after 2015.
- Publication volume rose sharply: 44.6% of included studies were published in 2021–2025, according to Mere et al.
What happened: how the review mapped framework use
Answer: The PLOS One scoping review documented who used which frameworks, when, and where, and produced a 14-category typology of frameworks used in community mental health process evaluations.
According to Mere et al. (PLOS One, 2026), the authors screened 1, 143 records and, after screening and eligibility checks, included 83 studies published between January 2006 and April 2025.
According to Mere et al. (PLOS One, 2026), those 83 studies reported 54 distinct primary frameworks organized into 14 categories, with implementation science frameworks dominating at 39.8% and the three most-used individual frameworks being RE-AIM (n = 9), CFIR (n = 8), and the MRC Framework (n = 6).
Quotation: Mere et al. titled their paper "From participation to systematization: A scoping review of theoretical frameworks guiding process evaluations in community mental health interventions" and explicitly concluded that "the field has undergone a paradigm shift from community-engaged to systematized evaluation approaches, " (Mere et al., PLOS One, 2026).
Findings snapshot
| Date or Period | Metric | Value | Implication |
|---|---|---|---|
| 2006–2025 | Included studies | 83 studies | Comprehensive mapping across two decades |
| 2006–2025 | Distinct primary frameworks | 54 frameworks | High fragmentation at the framework level |
| 2016–2020 vs 2006–2015 | Implementation science share | 14.3% → 52.0% | Rapid adoption of implementation science after 2015 |
| 2006–2015 | Participatory primary frameworks | 23.8% in early period; 0% after 2015 | Participatory approaches moved from primary to secondary roles |
| Published July 28, 2026 | Review publication date | Mere et al., PLOS One | Source for all numbers above |
Implications for researchers designing process evaluations
How should teams choose a framework for a community mental health process evaluation?
Answer: Choose a framework that maps to your primary evaluation question, because framework choice determines which constructs and stakeholders will be foregrounded.
According to Mere et al. (PLOS One, 2026), determinant frameworks like CFIR are best for identifying implementation barriers and facilitators, evaluation frameworks like RE-AIM suit reach and sustainability questions, and program theory approaches support testing intervention logic.
Recommendation: Explicitly justify the choice of framework in methods, and document which secondary frameworks you use, since Mere et al. found 73% of studies referenced at least one secondary framework.
What does the shift to implementation science mean for community participation?
Answer: The shift means community voice is often included as data rather than as a driving organizing principle.
According to Mere et al. (PLOS One, 2026), participatory frameworks were primary in 23.8% of early studies (2006–2015) but were absent as primary frameworks after 2015, although participatory approaches still appeared as secondary frameworks in later studies.
Implication: Researchers should consider hybrid strategies that embed participatory design within implementation science frameworks to preserve community authority while enabling comparability.
What equity and geographic considerations should researchers note?
Answer: Framework-generalizability is limited because most evidence comes from high-income settings.
According to Mere et al. (PLOS One, 2026), 54.2% of included studies originated in North America and 83.1% in high-income countries, indicating geographic concentration that limits transferability.
Action: Prioritize local adaptation and validate framework constructs before applying them in low- and middle-income country settings.
How Evidano helps: AI-enabled qualitative workflows for process evaluation
Problem: Fragmented frameworks make synthesis slow
Answer: Framework fragmentation slows comparative analysis because studies use different constructs and labels.
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Solution mapping: Evidano ingests transcripts, coding manuals, and framework documents, then auto-extracts themes and aligns qualitative codes to multiple framework constructs for cross-study comparison using thematic and cross-segment analyses.
Problem: Integrating participatory data into systematized evaluations
Answer: Teams struggle to surface community perspectives when implementation science frameworks dominate study design.
Feature: Evidano supports human-in-the-loop coding and collaborative codebooks so community co-researchers can contribute codes directly, preserving participatory inputs while enabling systematized outputs.
Contextual link: Learn more about collaboration and analysis features on the Evidano features page.
Problem: Large qualitative datasets and reproducible coding
Answer: Manual coding of large interview sets is time consuming and error prone.
Feature: Evidano offers AI-assisted transcription and PII redaction plus thematic and frequency analysis that produce reproducible codebooks and cross-segment visualizations; see Evidano speech-to-text and data security pages for technical details.
Benefit: Teams can move from raw interviews to framework-aligned syntheses in hours instead of weeks while keeping community-sourced codes traceable.
FAQ: qualitative process evaluation frameworks
What is a qualitative process evaluation framework and why does it matter?
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Answer: A qualitative process evaluation framework is a structured set of constructs that guides what implementation questions are asked and which data are collected.
According to Mere et al. (PLOS One, 2026), frameworks direct attention to implementation fidelity, reach, mechanisms, and context, and choosing a framework shapes both methods and interpretation.
When should I use an implementation science framework versus a participatory framework?
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Answer: Use an implementation science framework when your goal is structured, comparable measurement of determinants and outcomes; use a participatory framework when community partnership and co-design are primary goals.
According to Mere et al. (PLOS One, 2026), implementation science frameworks rose from 14.3% of studies in 2006–2015 to 52.0% in 2016–2020, reflecting a systematization trend that favors structured comparability.
How can AI help align qualitative data to multiple frameworks?
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Answer: AI can map codes and excerpts to framework constructs, surface co-occurrence patterns, and produce cross-segment frequency tables to support framework comparison.
Practical tip: Upload interview transcripts and framework definitions to an AI-enabled platform, review AI-proposed mappings, then finalize a reproducible codebook; Evidano supports this workflow and provides AI chat over your documents to query mappings interactively.
Are there risks to using systematized frameworks for community evaluations?
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Answer: Yes, the risk is that systematization can marginalize community authority if participatory elements are treated as optional.
According to Mere et al. (PLOS One, 2026), participatory frameworks moved from primary to secondary roles after 2015, which suggests teams should intentionally embed participatory processes rather than assume they will occur.
Conclusion & Next Steps
Answer: The PLOS One scoping review shows the field moved from participation to systematization between 2006 and 2025, creating both opportunities for comparability and risks for community voice.
According to Mere et al. (PLOS One, 2026), 83 studies used 54 primary frameworks and implementation science now dominates, but participatory approaches remain important as secondary elements.
Next steps: combine systematized implementation science constructs with participatory methods, validate frameworks in local contexts, and use AI tools to accelerate transparent, reproducible qualitative synthesis.
If you want to test this workflow on your project, Try Evidano for free.
Topics
- qualitative process evaluation frameworks
- process evaluation frameworks
- AI qualitative analysis
- implementation science process evaluation
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