AI-enabled qualitative analysis: postnatal diabetes

According to the PLOS ONE protocol by Phonyiam et al. (published July 29, 2026), an explanatory sequential mixed-methods study will survey 50 postnatal women with type 2 diabetes and interview 12 purposively sampled participants to link social determinants of health to postnatal well-being. PLOS ONE

• 50 participants will complete the quantitative survey, and 12 participants will be interviewed, recruitment began in September 2025 and is expected to finish by December 2026, with results projected by June 2027.
• The Thai translation of the Postnatal Well-being in Transition instrument achieved I-CVI and S-CVI values of 1.0 in the expert validation, as reported in July 2026.
• The study uses a three-step mixed-methods integration (fit assessment, procedural resolution, synthesis) to produce meta-inferences, following Fetters et al., 2013, as cited in PLOS ONE (Phonyiam et al., 2026).

What happened: Phonyiam et al. published a study protocol in PLOS ONE on July 29, 2026 that lays out an explanatory sequential mixed-methods study in two public hospitals in Thailand.

How it works: According to Phonyiam et al. (PLOS ONE, 2026), Phase 1 completed a six-stage cultural translation and validation of the Postnatal Well-being in Transition scale, and Phase 2 will collect quantitative data from 50 postnatal women and qualitative interviews with 12 nested participants selected from the survey extremes.

Measurement and timing: Phonyiam et al. (PLOS ONE, 2026) describe a 30-item postnatal well-being Likert instrument, a 20-item SDoH frequency tool, SPSS analysis for means and frequencies, Atlas.ti v9 for coding, and joint-display triangulation to integrate qualitative themes with quantitative means.

Constraints: Phonyiam et al. (PLOS ONE, 2026) note a limitation that the adapted SDoH instrument lacks formal cross-cultural psychometric validation for Thai populations, so findings will require cautious interpretation for other Southeast Asian contexts.

Implication summary: Phonyiam et al. (PLOS ONE, 2026) show that explanatory sequential mixed-methods yields both population-level patterns and contextual explanations, which is useful when social determinants interact with chronic disease transitions such as postpartum T2DM.

Design decisions: According to Phonyiam et al. (PLOS ONE, 2026), researchers should plan nested sampling to re-contact extreme quantitative cases (6 lowest and 6 highest well-being scores) to maximize explanatory depth and to code blind to scores during initial qualitative analysis to reduce bias.

Measurement guidance: Phonyiam et al. (PLOS ONE, 2026) recommend side-by-side joint displays and a three-step interpretive framework (fit assessment, procedural resolution of discordance, synthesis into meta-inferences) to reconcile convergence, expansion, and discordance between datasets.

Phonyiam et al. (PLOS ONE, published July 29, 2026) provide a transparent protocol showing how an explanatory sequential mixed-methods design can illuminate the social determinants of postnatal well-being for women with type 2 diabetes.

Researchers should plan for nested sampling, validated translations, and a documented integration strategy as described in the protocol.

If your team needs reproducible transcription, translation, thematic synthesis, and joint-display export for an explanatory sequential study, consider tools that integrate these steps to cut analysis time and strengthen audit trails.

To evaluate these workflows in your project, Try Evidano for free.