Rigorous Qualitative Analysis of Med Student Wellbeing

This protocol operationalizes a sequential-exploratory mixed-methods pipeline: first, a reproducible web-scrape and NLP-based content analysis of public medical school webpages; second, purposive KIIs with school wellbeing leads; third, student FGDs to surface lived experience; finally, triangulation to produce a standardized evaluative framework.

• Web scraping: custom Python crawler (requests + BeautifulSoup), sentence embeddings, similarity filtering.
• Extraction: fine-tuned BERT for targeted Q&A; c-TF-IDF + MMR for cluster labeling.
• Clustering & visualization: UMAP (n_neighbors=15, min_dist=0.1, n_components=2, random_state=42) then HDBSCAN (min_cluster_size=5, min_samples=3).
• Qual phase: coded in Dedoose with inter-coder reliability (Cohen’s kappa) and iterative codebook refinement.
• Integration: joint displays and comparative matrices to find convergence/divergence across data sources.

Follow these steps to implement the study architecture with AI support:

• 1) Select a reproducible sample and fix the random seed (they use a 50-school LCME sample).
• 2) Run a crawler to harvest page titles, URLs, and sentences; save metadata (publish dates, links).
• 3) Embed sentences (choose a stable model), filter by cosine similarity to question embeddings to keep relevant sentences.
• 4) Dimensionality reduction + HDBSCAN for clusters; extract c-TF-IDF labels and qualitatively validate cluster samples.
• 5) Use cluster labels to draft a codebook and recruit purposive KIIs and FGDs for contextual depth.
• 6) Transcribe audio, code with double-coding and kappa checks, and iterate the codebook until saturation.
• 7) Produce joint displays triangulating web-scrape frequencies with themes; share a standardized framework for program evaluation.

Godbole et al. (published June 18, 2026) provide a rigorous, reproducible template for evaluating medical student wellbeing that pairs automated web-scale capture with deep qualitative methods (journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0351759). If your team is responsible for program evaluation, accreditation evidence, or student experience research, you can reproduce this architecture faster with Evidano: ingest scraped pages, run embeddings and clustering, auto-seed codebooks, and manage secure transcription and team coding in a single platform.

Ready to turn protocol into production? Start a pilot with Evidano to run Phase I-equivalent scraping and thematic mapping in days, not months: www.evidano.com