Digital gender exclusion is a complex social problem that qualitative methods expose through interviews, focus groups, and co-design; the primary keyword is digital gender exclusion qualitative analysis. The PLoS One study protocol by Shahil-Feroz et al. (2026) investigates why marginalized women in Azam Basti, Karachi are offline and how co-designed solutions can promote inclusion. This post refracts that protocol through the lens of AI-enabled qualitative research, showing how AI transcription, translation, thematic coding, and cross-segment analysis can accelerate rigorous synthesis for researchers and program designers.
Key Takeaways
According to PLoS One (Shahil-Feroz et al., 2026), gendered social norms and household gatekeepers are central drivers of why marginalized women in Azam Basti, Karachi lack meaningful digital access, and the study tests a community-based participatory approach to co-design solutions.
- The PLoS One protocol plans a purposive qualitative sample of approximately 36–45 participants across marginalized women, family gatekeepers, and community leaders, with recruitment scheduled for August–November 2026.
- The PLoS One authors cite the GSMA Mobile Gender Gap Report 2024 showing women in Pakistan were 38% less likely than men to own a mobile phone or access mobile internet, and Pakistan ranked 145 of 146 in the 2024 Global Gender Gap Index, according to the study.
- The project begins February 2026 and expects data collection completed by April 2027 and results available beginning in late 2027, making rapid, transparent qualitative analysis essential for timely policy input.
- The protocol explicitly targets sociocultural and structural barriers beyond device access, arguing that “deeply entrenched sociocultural barriers” and family control require co-designed, household-level strategies (Shahil-Feroz et al., 2026).
What happened and how the study will work
Answer: The PLoS One protocol (Shahil-Feroz et al., 2026) lays out a three-phase community-based participatory research (CBPR) study in Azam Basti, Karachi that uses focus groups, in-depth interviews, and co-design workshops to explore digital gender exclusion.
According to Shahil-Feroz et al. in PLoS One (2026), Phase 1 forms a Community Advisory Committee (CAC) with two community leaders, two marginalized women, and three family gatekeepers to co-lead the work and validate instruments.
According to Shahil-Feroz et al. (2026), Phase 2 will run two focus group discussions per participant group (4–5 participants per group), plus 4–5 individual interviews per group, for an estimated total sample of 36–45 participants, using photo-elicitation for women and role-play for gatekeepers.
According to Shahil-Feroz et al. (2026), Phase 3 runs three co-design workshops with 8–10 participants each (25–30 total) to co-create 2–3 context-specific strategies, and the team will use reflexive thematic analysis with two independent coders and NVivo for organization.
According to Shahil-Feroz et al. (2026), study materials include images generated with an AI tool: "All images are generated through Open AI ChatGPT image generation tool", and transcripts will be produced in Urdu then translated into English for analysis.
Method constraints: The PLoS One authors note the single-community design may limit generalizability and that social desirability bias can shape responses; the team plans reflexive memos, CAC validation, and ethical safeguards including informed consent and multiple ethics approvals.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 2024 | GSMA Mobile Gender Gap metric cited in the protocol | Women in Pakistan 38% less likely than men to own a mobile phone or access mobile internet | Quantifies the access gap the qualitative study aims to explain at the household and cultural level |
| 2024 | Global Gender Gap Index (World Economic Forum) ranking cited | Pakistan ranked 145 of 146 countries in 2024 | Contextualizes deep structural gender inequalities shaping technology access |
| Feb 2026 | Project start (planning) | Two-year project begins Feb 2026; prep Feb–Jul 2026 | Sets timelines that require efficient transcription and analysis pipelines |
| Aug–Nov 2026 | Participant recruitment window | Recruitment expected Aug–Nov 2026 for ~36–45 participants | Highlights need for rapid, multilingual transcription and coding |
| Apr 2027 | Expected completion of data collection | Data collection completed by Apr 2027; results expected late 2027 | Signals a ~5 month analysis and dissemination window where AI tools speed synthesis |
Implications for qualitative researchers and program designers
Answer: The PLoS One protocol implies that rigorous, participatory qualitative research must combine culturally sensitive methods with timely analysis to inform policy and programs.
Practical implication 1: According to Shahil-Feroz et al. (2026), engaging family gatekeepers and community leaders shifts interventions from individual-level literacy programs to household and community norms change, which should change how researchers design sampling and facilitation.
Practical implication 2: According to Shahil-Feroz et al. (2026), photo-elicitation and role-play generate mixed-media data (images, enactments, audio) that require robust transcription, translation, and multimodal coding workflows to capture meaning across languages and registers.
Practical implication 3: According to Shahil-Feroz et al. (2026), co-design workshops produce iterative solution prototypes; researchers and NGOs should plan for rapid, transparent synthesis and stakeholder-ready outputs such as policy briefs and arts-based materials.
How Evidano helps: accelerating credible qualitative synthesis
Evidano definition
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Problem: Multilingual transcription and translation. Solution: Evidence from the PLoS One protocol shows transcripts will be in Urdu and translated to English; Evidano offers automated speech-to-text with custom dictionaries and translation to speed accurate bilingual workflows and reduce backlogs.
Problem: Volume of heterogeneous data (FGDs, role-play recordings, photo-elicitation). Solution: Evidano organizes audio, transcripts, and images into searchable corpora and applies thematic coding plus cross-segment frequency analysis to compare marginalized women, gatekeepers, and leaders.
Problem: Need for rapid team coding and community validation. Solution: Evidano supports collaborative codebooks, hierarchical codes→subcodes visualizations, and AI-assisted thematic summaries to accelerate reflexive thematic analysis while preserving audit trails for rigor.
Problem: Tight timelines for policy-ready outputs. Solution: Evidano’s exportable dashboards and AI chat over your documents enable researchers to produce stakeholder briefs and visualizations faster; see the product features.
Problem: Manual transcription bottleneck. Solution: Evidano’s speech-to-text pipeline with PII redaction and custom dictionaries fits the protocol’s need for secure, accurate Urdu→English transcription and speeds analysis.
FAQ: digital gender exclusion qualitative analysis
How can qualitative methods identify why marginalized women are offline?
Answer: Qualitative methods reveal beliefs, power relations, and household decision rules that explain why women lack access.
According to Shahil-Feroz et al. in PLoS One (2026), focus groups with photo-elicitation and role-play with gatekeepers uncover norms and safety concerns that surveys miss, enabling actionable co-designed solutions.
What sample size and designs are appropriate for this question?
Answer: Purposeful, small-sample CBPR designs that reach theoretical saturation are appropriate for in-depth understanding.
The PLoS One protocol plans approximately 36–45 participants across FGDs and interviews and notes saturation will guide the final counts, which matches qualitative best practice for exploratory, community-rooted work (Shahil-Feroz et al., 2026).
How should teams manage Urdu audio, translations, and coding at scale?
Answer: Use secure, automated transcription and bilingual review workflows plus AI-assisted coding to preserve nuance and speed analysis.
The PLoS One team will transcribe in Urdu and translate to English for reflexive thematic analysis; platforms that offer custom dictionaries, PII redaction, and bilingual verification reduce error and support reproducibility.
Can AI tools be used ethically in participatory research with marginalized groups?
Answer: Yes, if teams apply strong ethics, informed consent, and data governance safeguards.
The PLoS One protocol obtained ethics approvals and emphasizes consent and confidentiality; similarly, researchers should use encrypted platforms, obtain explicit consent for recording and AI-assisted processing, and include community advisory review for outputs.
Conclusion & Next Steps
The PLoS One protocol by Shahil-Feroz et al. (2026) makes clear that sociocultural norms and household gatekeepers drive much of the digital gender exclusion observed in Pakistan, and that participatory, co-designed interventions are required to change those norms.
For qualitative researchers and implementers working on digital inclusion, combining rigorous CBPR methods with AI-enabled transcription, translation, and thematic analysis shortens the path from fieldwork to policy-ready recommendations.
If you are planning a similar study and want secure, bilingual transcription, shared codebooks, and AI-assisted thematic synthesis, explore how Evidano integrates transcription, translation, and collaborative analysis in one platform: Try Evidano for free.
Topics
- digital gender exclusion qualitative analysis
- gender digital exclusion Pakistan
- qualitative methods digital inclusion
- AI qualitative analysis
Keep reading
- Commentary on NewsCo-designing Inclusion: Digital Gender Exclusion Qualitative AnalysisPLOS One protocol on Pakistan's digital gender exclusion qualitative analysis shows CBPR methods and co-design; discover AI-enabled approaches to analyze and scale findings.
- Commentary on NewsAI-assisted Qualitative Analysis: Digital Gender ExclusionAI-enabled qualitative analysis for community research on digital gender exclusion in Pakistan: methods, dates, and AI tools to speed thematic insights and policy impact.
- Commentary on NewsAI-assisted qualitative analysis: digital gender exclusionHow AI-enabled qualitative analysis accelerates community-led research on digital gender exclusion. Use cases, numbers from PLOS One, and next steps with Evidano.