Mental health screening and risk factor analysis
Measurement, Drivers, and System Insight
Established a scalable framework for assessing student mental health and uncovering key behavioural and environmental drivers of wellbeing. By linking risk patterns to academic, family, and lifestyle factors, it enabled more targeted and preventative responses within schools.
At a glance
Limitations of existing approaches
- Screening often functions as one-off detection, with limited insight into underlying causes
- Psychological risk is typically treated as individual-level, without sufficient attention to environmental and behavioural drivers
Core need
A scalable approach that not only identifies at-risk students, but also captures the drivers of wellbeing, enabling more informed and preventative responses at the school level.
Screening (identification)
Systematically identify students at elevated psychological risk to support timely follow-up and prioritisation of limited school resources.
Measurement (baseline profiling)
Establish a structured, population-level baseline of student mental health across multiple dimensions, enabling comparison across age, gender, and cohort groups.
Driver analysis (explanatory layer)
Examine how academic pressure, family context, and lifestyle factors relate to mental health outcomes, moving beyond detection toward understanding underlying contributors.
System insight (application)
Generate evidence that can inform both individual-level intervention and broader school-level decision-making, bridging screening outputs with actionable response strategies.
Core scales
Standardised mental health and stressor measures to ensure reliability and comparability across participants.
Dimensions
Multi-dimensional coverage including anxiety, interpersonal sensitivity, somatic symptoms, and behavioural tendencies.
Contextual variables
Additional items capturing academic pressure, family environment, and lifestyle factors to enable analysis of potential drivers of wellbeing.
Scale
~1,488 students (ages 10–17) separated into two year groups.
Delivery
Browser-based system (computer-first), with mobile as supplementary access to ensure coverage and accessibility.
Data quality control
Removal of invalid responses (~10%) using validity checks to improve reliability and reduce response bias.
Tools
R used for structured statistical analysis.
Approach
Combined descriptive stats, group comparisons, and regression to detect patterns.
Focus
Identifying population-level trends and differences across age, gender, and context, and exploring potential contributors to overall mental health through relationships between stressors and outcomes.
Risk distribution
Identified both at-risk and borderline groups requiring attention.
Group differences
Older students showed consistently higher psychological risk levels.
Key drivers
Academic pressure, internal psychological strain, and family environment emerged as dominant factors.
Risk as a spectrum
Findings showed distributed levels of vulnerability, supporting tiered intervention approaches rather than binary classification.
Developmental effects
Higher risk in older cohorts suggested accumulating psychological strain, highlighting the importance of early intervention.
Interacting drivers
Internal symptoms and external pressures emerged as interdependent factors, requiring coordinated responses beyond individual counselling.
From detection to system insight
The system surfaced broader stress patterns (e.g. workload, environment), enabling a shift from reactive support to preventative, system-level thinking.
Self-administered online assessment platform, browser-based with mobile as supplementary access
School-level analytical reporting and statistical analysis
Stakeholder presentation of findings
Individual student reports for targeted follow-up