Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The PLOS ONE study by Shahil-Feroz et al. (2026) examines why marginalized women in Azam Basti, Karachi, are excluded from digital spaces and proposes participatory co-design to address norms-based barriers. AI-enabled qualitative research can speed transcription, translation, thematic coding, and cross-segment comparisons so researchers and policymakers can act on concrete, community-led solutions faster.
Key Takeaways
According to the PLOS ONE study PLOS ONE, sociocultural and household gatekeepers drive gendered digital exclusion among marginalized women in Karachi, and community co-design with gatekeepers is central to the study’s proposed solutions.
Reflexive thematic analysis of roughly 36–45 planned participants, with co-design workshops and a Community Advisory Committee, aims to produce contextually appropriate interventions beginning in late 2027, according to Shahil-Feroz et al. (2026).
- 1) According to the PLOS ONE study published 20 August 2026, Pakistan ranked 145 of 146 countries on the 2024 Global Gender Gap Index.
- 2) According to the PLOS ONE study citing GSMA, women in Pakistan were 38% less likely than men to own a mobile phone or access mobile internet in the GSMA Mobile Gender Gap Report 2024.
- 3) According to the PLOS ONE study, the planned field sample is approximately 36–45 participants and the two-year project starts in February 2026 with data collection scheduled August–November 2026.
- 4) According to the PLOS ONE study, the project received an SSHRC Insight Development Grant of $70, 626.00 to support community-based participatory research activities.
What Happened: study design and methods in plain terms
The PLOS ONE study answers who, what, when, and how: Shahil-Feroz et al. (2026) will use community-based participatory research in Azam Basti, Karachi, to study barriers to women's access to digital technology and co-design solutions with family gatekeepers and community leaders.
The PLOS ONE study (Shahil-Feroz et al., 2026) plans three phases: formation of a Community Advisory Committee with 7 members, focus group discussions and 4–5 in-depth interviews per participant group totaling about 36–45 participants, and three co-design workshops involving 25–30 participants.
The PLOS ONE study (Shahil-Feroz et al., 2026) will collect data in Urdu (and Sindhi when required), transcribe in-language, translate to English, and apply Braun and Clarke’s reflexive thematic analysis using NVivo for coding.
The PLOS ONE study notes ethical approvals from Western University NMREB, Aga Khan University, and Pakistan’s National Bioethics Committee, and the authors warn that findings from one community may not generalize across Pakistan.
Findings Snapshot
| Date / Phase | Metric | Value (from PLOS ONE) | Implication |
|---|---|---|---|
| 20 August 2026 | Publication date | PLOS ONE study published | Protocol available for replication and policy use |
| 2024 (GSMA) | Gender phone gap | Women 38% less likely to own/access mobile internet | Highlights structural access gap to target |
| February 2026–January 2028 | Project timeline | 2-year project with data collection Aug–Nov 2026 | Analysis and dissemination expected late 2027 onward |
| Study budgeting | Grant funding | $70, 626.00 (SSHRC Insight Development Grant) | Supports CBPR activities and community compensation |
| Sample | Participants | Approximately 36–45 across FGDs, interviews, workshops | Sufficient for reflexive thematic saturation per design |
Implications for researchers and program designers
Researchers should expect that sociocultural norms and household decision-makers are primary drivers of digital exclusion, according to Shahil-Feroz et al. (2026).
Program designers should prioritize household- and community-level engagement rather than only individual digital skills training, because Shahil-Feroz et al. (2026) argue that 'deeply entrenched sociocultural barriers' limit women's technology access.
Researchers planning CBPR in similar settings should budget for translation, in-language transcription, community advisory time, and iterative co-design workshops, because the PLOS ONE protocol schedules CAC meetings quarterly and compensates CAC members (PKR 4500, ~16 USD) per sessions.
How Evidano Helps: map from problem to AI-enabled solution
Problem: Slow transcription and translation of Urdu/Sindhi interviews
Answer: Evidence synthesis is delayed when manual transcription and bilingual translation create bottlenecks.
Solution: Evidano integrates automated transcription with custom dictionaries and supports translation workflows to produce aligned transcripts in English while preserving original text for auditability. See the speech-to-text feature for transcription capabilities.
Problem: Synthesis of multi-modal qualitative data (photos, role-plays, FGDs)
Answer: Multi-modal data increases complexity for theme generation and cross-segment comparison.
Solution: Evidano performs thematic coding and cross-segment frequency analysis across interviews, FGDs, and visual prompts and offers co-occurrence and hierarchical code visualizations to reveal how gatekeepers and women differ in themes.
Problem: Iterative co-design requires rapid feedback to communities
Answer: Slow analysis delays co-design cycles and reduces community ownership.
Solution: Evidano provides near-real-time AI-assisted thematic summaries and an AI chat over your documents to generate accessible briefings and stakeholder reports for community advisory committees, aligning with the PLOS ONE study’s CBPR aims. Learn more on Evidano features.
Problem: Data security and ethical concerns for sensitive qualitative data
Answer: Storing and processing interviews with marginalized participants requires strong privacy controls.
Solution: Evidano encrypts data at rest and in transit and guarantees data is never used to train external LLMs; see our data security page for compliance details.
FAQ: AI-enabled qualitative research
How can AI speed reflexive thematic analysis for a CBPR study?
Answer: AI can accelerate coding, produce candidate themes, and summarize transcripts while preserving reflexivity for researcher interpretation.
Supporting detail: According to the PLOS ONE protocol (Shahil-Feroz et al., 2026), reflexive thematic analysis requires iterative human interpretation; AI should therefore be used to generate initial codes and frequency counts that human analysts then refine.
Can AI handle transcription and translation of Urdu or Sindhi interviews?
Answer: Yes, modern AI-enabled pipelines can transcribe Urdu and translate it to English with human-in-the-loop review.
Supporting detail: The PLOS ONE study (Shahil-Feroz et al., 2026) transcribes in the original language and translates to English for analysis, which matches best practice of automated transcription plus bilingual quality checks.
How do you preserve community ownership when using AI tools?
Answer: Preserve community ownership by sharing AI-generated summaries with advisory committees and using AI outputs as discussion prompts, not final interpretations.
Supporting detail: Shahil-Feroz et al. (2026) co-analyse data with their Community Advisory Committee to validate themes, a method compatible with AI-assisted workflows when outputs are audited and co-validated.
Is AI analysis ethical for sensitive qualitative data?
Answer: AI analysis can be ethical if data is encrypted, PII is redacted, and participants understand how data will be processed.
Supporting detail: The PLOS ONE study obtained multi-jurisdictional ethics approvals and uses consent procedures in Urdu, which indicates that AI tools must mirror ethical safeguards such as consent, access controls, and audit trails.
Conclusion & Next Steps
The PLOS ONE protocol by Shahil-Feroz et al. (2026) shows that addressing Pakistan’s digital gender gap requires analysis of sociocultural norms and direct engagement with family gatekeepers to co-design solutions.
AI-enabled qualitative research platforms can shorten transcription and coding time, produce transparent thematic outputs, and support iterative co-design without replacing human reflexivity or community validation.
If you are planning CBPR or policy-driven qualitative work similar to the PLOS ONE study, use AI to accelerate analysis and preserve time for community engagement and validation.
For a practical next step, Try Evidano for free to upload transcripts, run thematic analyses, and produce shareable reports for advisory committees.
Sources and quotations
Primary source: Shahil-Feroz A, Asim M, Liaquat S, Meherali S, Allana S, Baruah B (2026) PLOS ONE.
Direct quote from Shahil-Feroz et al. (2026): "deeply entrenched sociocultural barriers" shapes marginalized women’s technology access (PLOS ONE, 20 August 2026).
Direct quote from the PLOS ONE protocol citing the UN definition: "equitable, meaningful, and safe access to use, lead, and design of digital technologies, services, and associated opportunities for everyone, everywhere" (United Nations, cited in PLOS ONE).
Topics
- AI-enabled qualitative research
- qualitative analysis with AI
- digital gender inclusion Pakistan
- thematic analysis AI
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