Abstract
The RTW Complexity Risk Model produces a continuously updated 0–100 risk score that quantifies the complexity of an individual return-to-work case. It is a knowledge-engineered, rule-based scoring system — not a machine learning model — designed for explainability, auditability, and defensibility in Canadian compliance contexts.
The model’s evidence base comprises 48 curated sources across 10 jurisdictions (Canada-first, then comparable jurisdictions: Australia, UK, New Zealand, Nordic countries, and the Netherlands), with 46 structured predictor→outcome evidence extracts. Every scoring rule traces back to specific evidence through a structured pipeline: source → extract → factor → rule → score change.
The scoring engine uses 5 domains encompassing 20 factors, with 30 score update rules (event-driven and time-driven). Compliance maturity across CSA Z1003 and Z1011:20 acts as an organizational multiplier — low maturity amplifies individual case risk; high maturity dampens it. This is decision-support only, not a clinical instrument or diagnostic.
Key Findings
What The Research Shows
- 1
5 domains: Supervisor Capability, RTW Process Quality, Work Design/Context, Compliance/System Maturity, Culture/Team Climate — each with 4 factors
- 2
30 score update rules: event-driven (something happened) and time-driven (something didn’t happen within an evidence-based window)
- 3
Compliance multiplier maps 30 elements from CSA Z1003 (15) and Z1011:20 (15) — organizational maturity amplifies or dampens individual case risk
- 4
Rule-based by design: every score change is traceable to a specific rule, factor, evidence extract, and source — no black boxes
- 5
Dual-trigger design captures both positive momentum (actions taken) and drift and delay (actions not taken)
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