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 by Mere et al. (2026), the community mental health process evaluation literature shifted from participatory, community-rooted frameworks toward implementation science frameworks between 2006 and 2025. The primary keyword for this piece, process evaluation frameworks, guides researchers, evaluators, and funders who need a concise map of what changed, when it changed, and how AI-enabled qualitative research tools can accelerate rigorous, community-attentive synthesis.
Key Takeaways
According to the PLOS One scoping review by Mere et al. (published July 28, 2026) PLOS One, the field of community mental health process evaluation moved from participatory frameworks toward implementation science frameworks between 2006 and 2025.
Mere et al. (2026) found both gains and losses in that shift: greater methodological consistency but reduced prominence of community-led frameworks as primary guides.
- 83 studies published between January 2006 and April 2025 met inclusion criteria in the scoping review, according to Mere et al. (PLOS One, July 28, 2026).
- Mere et al. (PLOS One, 2026) identified 54 distinct primary frameworks organized into 14 categories and reported that implementation science frameworks comprised 39.8% of primary frameworks.
- Mere et al. (PLOS One, 2026) observed that participatory and community-based frameworks accounted for 23.8% of studies in 2006–2015 but were absent as primary frameworks after 2015.
- Mere et al. (PLOS One, 2026) reported geographic concentration: 54.2% of included studies came from North America and 83.1% from high-income countries.
What happened and how the review measured it
Answer: Mere et al. mapped framework use in community mental health process evaluations by systematically reviewing peer-reviewed studies from 2006 to April 2025.
According to Mere et al. (PLOS One, July 28, 2026), the authors searched PubMed, Web of Science, and EBSCOhost, screened 1, 143 records, and retained 83 studies that explicitly reported process evaluations in non-clinical community settings.
According to Mere et al. (PLOS One, 2026), the review extracted framework names, study context, design features, and whether frameworks were primary or secondary; frameworks were then inductively grouped into a 14-category typology.
Mere et al. (PLOS One, 2026) divided the timeline into three periods for trend analysis: 2006–2015, 2016–2020, and 2021–2025, linking the 2015 Medical Research Council guidance to shifts in practice.
Findings snapshot
| Date / Period | Metric | Value | Implication |
|---|---|---|---|
| 2006–2015 | Studies using participatory frameworks as primary | 23.8% | Early literature foregrounded community-partnered designs, per Mere et al. (PLOS One, 2026). |
| 2016–2020 | Implementation science as primary frameworks | 52.0% | Rapid growth of implementation science frameworks in the middle period. |
| 2021–2025 | Share of all included studies published | 44.6% | Nearly half of included studies were published in 2021–2025, reflecting rising attention to process evaluation. |
| 2006–2025 (entire sample) | Distinct primary frameworks identified | 54 frameworks across 83 studies | High fragmentation at the individual-framework level despite categorical consolidation. |
Implications for qualitative researchers and evaluators
How should I choose a process evaluation framework?
Answer: Choose the framework that aligns with your primary evaluation question, not the most popular label.
According to Mere et al. (PLOS One, 2026), determinant frameworks such as CFIR suit investigations of contextual barriers and facilitators, evaluation frameworks such as RE-AIM suit questions about reach and sustainability, and program theory approaches suit testing causal logic.
According to Mere et al. (PLOS One, 2026), many studies (73%, n = 61) used secondary frameworks, which suggests combining frameworks can cover blind spots but requires explicit justification in methods sections.
What does the decline of participatory primary frameworks mean for community voice?
Answer: The decline suggests community partnership has been repositioned rather than fully abandoned, but epistemic authority may have shifted toward researcher-led systematization.
According to Mere et al. (PLOS One, 2026), participatory frameworks appeared as primary in five early studies (all 2006–2015) and were absent as primary frameworks after 2015, though participatory approaches appeared as secondary frameworks in later work.
Are the dominant frameworks globally generalizable?
Answer: Not necessarily; the evidence base is concentrated in high-income settings.
According to Mere et al. (PLOS One, 2026), 83.1% of included studies came from high-income countries and only 14.4% from low- and middle-income countries, raising external validity and equity concerns.
How Evidano helps with process evaluation framework synthesis
Problem: Fragmented frameworks slow synthesis
Answer: Fragmentation across 54 frameworks in 83 studies makes manual mapping slow and error prone.
According to Mere et al. (PLOS One, 2026), most individual frameworks appear in only one or two studies, which complicates cross-study comparison and meta-synthesis.
Solution: AI-enabled thematic mapping
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano automates thematic extraction and cross-segment frequency analysis so teams can rapidly map which framework constructs appear across transcripts, coded sections, and study reports.
Evidano’s AI chat over your documents accelerates iterative framework selection by answering questions like, "Which CFIR constructs appear in these 25 process evaluation reports? " and by producing exportable code→subcode hierarchies for team review.
For teams that start with audio or multilingual data, Evidano supports speech-to-text transcription with custom dictionaries and translation options to keep community quotes intact while preserving PII controls.
Solution: Transparent, auditable outputs for funders and journals
Answer: Evidano generates shareable visualizations and auditable codebooks to document how framework constructs were identified.
Evidano produces word clouds, co-occurrence networks, and hierarchical code→subcode visualizations helping you show reviewers and funders how participatory elements, implementation determinants, and fidelity metrics were operationalized.
FAQ: process evaluation frameworks
What are process evaluation frameworks and why do they matter?
Answer: Process evaluation frameworks are conceptual tools that specify which aspects of implementation to measure and interpret.
According to Mere et al. (PLOS One, 2026), frameworks direct attention to implementation fidelity, reach, context, mechanisms, and stakeholder perspectives and therefore shape what data evaluators collect and report.
Did the PLOS One review find that participatory methods disappeared entirely?
Answer: No, participatory methods were repositioned rather than erased.
According to Mere et al. (PLOS One, 2026), participatory frameworks were used as primary frameworks only in the 2006–2015 period but continued to appear as secondary frameworks in later studies, indicating a shift from organizing framework to supplementary role.
Which specific frameworks were most common?
Answer: RE-AIM, CFIR, and the MRC process evaluation framework were the three most commonly used individual frameworks.
According to Mere et al. (PLOS One, 2026), RE-AIM appeared in nine studies, CFIR in eight studies, and the MRC Framework in six studies, together accounting for 27.7% of included studies.
How can I preserve community voice while using implementation science frameworks?
Answer: Integrate participatory methods explicitly as co-design or governance elements and document them as primary design features.
Mere et al. (PLOS One, 2026) recommend integrating participatory elements rather than treating community engagement as an afterthought; tools that code stakeholder roles and power relations help demonstrate meaningful participation.
Can AI help me compare framework use across dozens of studies?
Answer: Yes, AI-assisted qualitative analysis can extract framework constructs, count occurrences, and cross-tabulate by region or intervention domain.
Evidano automates thematic coding and cross-segment frequency tables, reducing weeks of manual coding to hours and producing auditable outputs for publication and funder reporting.
Conclusion & Next Steps
Mere et al.’s PLOS One scoping review (July 28, 2026) documents a measurable shift in community mental health process evaluation from participatory primary frameworks to implementation science systematization between 2006 and 2025.
Researchers should match framework choice to evaluation questions, make secondary frameworks explicit, and prioritize equity and context when exporting frameworks across settings, as argued by Mere et al. (PLOS One, 2026).
Evidano speeds these activities by automating thematic synthesis, generating cross-study frequency and co-occurrence analyses, and producing auditable codebooks for reviews and funders; see our features page for details.
If you need to map framework constructs across reports, preserve community quotations, and produce transparent outputs for publication, Try Evidano for free.
"From participation to systematization, " as Mere et al. (PLOS One, 2026) titled their review, is a prompt: the next step is 'systematized participation', and AI-enabled qualitative research tools can help the field get there.