Research & Data Consulting Case Studies
A selection of case studies demonstrating how complex research and data challenges are translated into structured analysis, clear insight, and actionable outcomes.
Strategic Data Problem-Solving
Designing Reliable Clinical Data Systems for AI-Ready Decision-Making (Mental Health Data Analysis)
When unclear definitions made sensitive mental health data unreliable, we designed a structured interpretation framework to eliminate ambiguity, ensuring consistency, trust, and readiness for AI and clinical use.
Advanced Statistical Analysis
Statistical Analysis of Intensive Care Unit (ICU) Data for Defensible Clinical Evidence
We transformed a high risk ICU dataset into a rigorous analytical framework, ensuring statistically valid, clinically meaningful conclusions that could withstand peer review and inform real-world decisions.
Qualitative Insight / Strategy
Qualitative Analysis of Decision-Making in Complex Investment Environments
We conducted qualitative analysis to assess how practitioners think under uncertainty and translated this into a structured decision-making framework that enabled clearer, more confident investment strategy.
Research Design / Framework Development
Research Design & Data Framework Development for Data-Scarce Systems
In a fragmented fisheries sector, we built a structured research and data framework that transformed uncertainty into actionable insight, guiding investment and policy decisions.
Real-World Impact / Programme Evaluation
Programme Evaluation & Health Data Analysis for Evidence-Based Impact
We designed and delivered a mixed-methods evaluation that demonstrated measurable health improvements and enabled confident decisions on programme scaling and funding.
Systematic Review and META-ANALYSIS
Resolving Conflicting Evidence Through Systematic Review and Meta-Analysis
The literature contained inconsistent findings across studies, making it difficult to draw reliable conclusions. We conducted a structured systematic review and meta-analysis to generate defensible, publication-ready evidence.
Featured Case Study
Understanding Social Support in Online Diabetes Communities
Mixed-Methods Research & Machine LearningExploring how individuals seek and provide social support in an online diabetes community, taking into consideration users' progressive disease state by using advanced analytical methods.
Research Challenge in Online Health Data Analysis
Online health communities have rich, unstructured text data on patients experience. The challenge was to understand how different types of support were expressed, how support varied across stages of the condition, and how these interactions could inform research and practical interventions.
Mixed-Methods and Machine Learning Approach
Development of a mixed-methods analytical framework that combined qualitative research with supervised machine learning techniques to retain depth of insight while enabling scalable analysis to identify, quantify, and compare patterns of social support.
Statistical Analysis and Machine Learning Implementation
- Defined and refined social support categories through qualitative analysis
- Built and annotated a training dataset
- Engineered linguistic and behavioural features
- Developed and evaluated machine learning models
- Assessed patterns across diabetes stages
1. Enabled large scale analysis of social support exchanges in an online diabetes community.
2. Revealed patterns in how individuals seek and provide support across diabetes progressive stages.
3. Provided a framework applicable to other chronic conditions and online health communities.
Key Insight
Online diabetes communities function as dynamic ecosystems of social support, where users transition from help seekers to knowledge providers as they gain experience and control over their condition. When analysed effectively, they reveal how patient needs shift over time — enabling the design of more precise, data-informed support and patient engagement strategies.
User Data
Posts
Feature Extraction
Text signals
Model
Classification
Output
Support types
Transforming unstructured data into reliable, decision-ready insight.
Published in peer-reviewed journal.
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