Researchers and evaluators designing process evaluations in community mental health need clear guidance on which theoretical frameworks to use and why. The primary keyword for this post is process evaluation frameworks community mental health, because the literature now shows a notable shift in which frameworks are used and how community voice is positioned. According to Mere et al. (2026) in PLoS One, that shift spans studies published between January 2006 and April 2025 and has concrete implications for study design, stakeholder engagement, and cross-study synthesis. This post refracts the PLoS One findings through the lens of AI-enabled qualitative research and offers practical steps for teams who must analyze interviews, open-ended survey responses, and implementation documents.
Key Takeaways
According to the PLoS One article From participation to systematization: A scoping review of theoretical frameworks guiding process evaluations in community mental health interventions, the field shifted from participatory frameworks toward implementation science between 2006 and 2025, with important tradeoffs for community voice and methodological consistency.
- 83 studies used 54 distinct primary frameworks across 2006–2025, according to the PLoS One review published on July 28, 2026.
- Implementation science frameworks accounted for 39.8% of primary frameworks (n = 33) across the sample, according to Mere et al. (2026).
- Participatory approaches were 23.8% of studies in 2006–2015 and disappeared as primary frameworks after 2015, according to the PLoS One article.
- North America produced 54.2% of included studies and high-income countries contributed 83.1% of studies, according to the PLoS One review.
- Mere et al. (2026) summarize the trend as a shift “from participation to systematization” and warn that community‑centred frameworks have been repositioned rather than integrated.
What happened and how the review measured it
Answer: The scoping review documented a temporal and geographic reorientation of process evaluation frameworks in community mental health between 2006 and April 2025, using systematic searches and inductive typology development, according to Mere et al. (2026) in PLoS One.
According to the PLoS One study, the authors searched PubMed, Web of Science, and EBSCOhost and screened 1, 143 records to identify 83 eligible studies published between January 2006 and April 2025, and then extracted framework names, contexts, and methods for each included paper.
According to Mere et al. (2026), the 54 distinct primary frameworks were organized into 14 categories through iterative coding, and temporal trends were compared across three periods: 2006–2015, 2016–2020, and 2021–2025.
Direct quote: "We identified 83 studies employing 54 distinct primary frameworks, " (Mere et al., 2026, PLoS One).
Findings snapshot
| Date or period | Metric | Value (from PLoS One) | Implication |
|---|---|---|---|
| 2006–2025 | Included studies | 83 studies | Substantial growth in process evaluation attention in community mental health |
| 2006–2025 | Primary frameworks identified | 54 distinct frameworks | High fragmentation at the framework level |
| 2006–2025 | Framework categories | 14 categories | Typology enables targeted framework selection |
| 2006–2025 | Implementation science share | 39.8% (n = 33) | Implementation science is the dominant orientation |
| 2006–2015 vs post-2015 | Participatory as primary framework | 23.8% in 2006–2015; none as primary after 2015 | Participatory approaches were repositioned away from primary organizing frameworks |
| Geographic (2006–2025) | North America share | 54.2% | Results are concentrated in high-income Western contexts |
Implications for qualitative researchers and evaluators
Answer: Researchers should select frameworks based on intended evaluation questions and be explicit about what a chosen framework omits, according to the PLoS One typology and its authors.
According to Mere et al. (2026), implementation science frameworks are well suited when the evaluation question prioritizes determinants, reach, adoption, fidelity, and sustainment, whereas participatory frameworks foreground partnership, empowerment, and community-defined outcomes.
According to the PLoS One article, the shift toward implementation science increased comparability but risked reducing community epistemic authority, so evaluators should consider hybrid designs that combine systematization with meaningful participation.
Practical checklist for framework selection, informed by the PLoS One typology:
- If your priority is identifying implementation determinants, map your study to determinant frameworks such as CFIR, as suggested by Mere et al. (2026).
- If your priority is public health impact and scalability, consider evaluation frameworks such as RE-AIM, per the PLoS One findings.
- If your priority is community empowerment and co‑production, retain participatory methods as primary design elements or explicitly integrate them as core constructs rather than secondary notes, following community-engaged evaluation principles.
How Evidano Helps: AI-enabled qualitative research for process evaluations
Definition and fit
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano supports process evaluation workflows by automating transcript processing, generating thematic and cross-segment analyses, and enabling rapid re-coding when theoretical lenses change during iterative evaluation, which is useful when teams move between participatory and implementation science perspectives.
Problem: Fragmented frameworks and evolving questions → Solution: Dynamic thematic and typology mapping
Problem: The PLoS One review found 54 frameworks across 83 studies, creating fragmentation that complicates synthesis, according to Mere et al. (2026).
Solution: Evidano’s thematic and hierarchical code maps let teams import documents and then reapply alternate frameworks (for example CFIR vs RE-AIM vs CBPR) to the same corpus, enabling empirical comparison across frameworks without re-collecting data.
See Evidano features for details on AI thematic analysis and code hierarchies.
Problem: Large qualitative corpora from multi-stakeholder evaluations → Solution: Transcription, translation, and AI chat
Problem: Process evaluations often include interviews, focus groups, field notes, and meeting minutes across stakeholders; Mere et al. (2026) note that mixed methods designs predominate, increasing analytic complexity.
Solution: Evidano provides accurate speech-to-text with custom dictionaries and PII redaction, plus translation and an AI chat over your documents so teams can query themes, segment counts, and illustrative quotes in seconds.
Problem: Need to preserve community voice while systematizing → Solution: Cross-segment and participatory reporting
Problem: The PLoS One review documents a decline of participatory frameworks as primary organizers after 2015, raising risks that community perspectives become secondary (Mere et al., 2026).
Solution: Evidano supports cross-segment frequency and co-occurrence analyses and can generate stakeholder-specific codebooks and exportable visualizations that make community input auditable and central to reporting.
FAQ: process evaluation frameworks community mental health
What are the most common framework categories used in community mental health process evaluations?
Answer: Implementation science frameworks are the most common category, accounting for 39.8% of primary frameworks in the review by Mere et al. (2026) in PLoS One.
Supporting detail: Within implementation science, determinant frameworks such as CFIR and evaluation frameworks such as RE-AIM were among the most used individual approaches, according to the PLoS One article.
Did participatory frameworks disappear after 2015?
Answer: Participatory frameworks stopped appearing as primary organizing frameworks after 2015, but they continued to appear as secondary frameworks in later studies, according to Mere et al. (2026).
Supporting detail: The PLoS One review reports participatory approaches at 23.8% in 2006–2015 and zero primary uses after 2015, suggesting repositioning rather than total abandonment.
How should I choose a framework for my next process evaluation?
Answer: Choose a framework that aligns tightly with your core evaluation question and explicitly document why other frameworks were not chosen, as recommended by Mere et al. (2026).
Supporting detail: Use determinant frameworks for barriers and facilitators, RE-AIM for population impact and reach, and program theory approaches when you need to test causal pathways; consider hybrid designs to retain community participation.
Can AI help compare what different frameworks reveal from the same data?
Answer: Yes, AI-enabled qualitative platforms can re-code the same corpus under different frameworks and produce comparative summaries, reducing the analytic work required for empirical framework comparisons.
Supporting detail: The PLoS One authors call for empirical comparisons of frameworks; Evidano’s thematic re-mapping and cross-segment analyses are designed to support exactly that workflow. See Evidano features.
Conclusion & Next Steps
The PLoS One scoping review by Mere et al. (2026) documents a marked shift from participatory to systematized, implementation science frameworks in community mental health process evaluations between 2006 and April 2025, and it calls for tools and methods that preserve community voice while enabling comparability.
If your team needs to analyze interviews, focus groups, and documents and to test how different frameworks shape findings, AI-enabled thematic re-mapping shortens the work and makes comparisons auditable.
To try these workflows yourself, explore how Evidano handles transcription, thematic analysis, and cross-segment reporting; Try Evidano for free.