About Prior Work

Practitioner-led psychosocial risk analytics, grounded in decades of work stress research.

What is this?

A practitioner-led psychosocial risk management tool built on complex data modelling techniques. Prior Work shows how psychosocial factors interact with each other to shape health, wellbeing, and engagement, and turns that picture into a workable risk management cycle - the kind of analysis the Codes of Practice ask for, run by your own team rather than outsourced to a consultant. Optional adaptive reasoning AI models generate actionable, professional report summaries of existing workplace conditions.

The name carries a double meaning. The prior work your organisation has already done - the survey, the data - is where the analysis starts. “Prior” is also a nod to a way of seeing the world we’re fond of: you begin with what you already know and let new evidence revise it, and you treat work as it really is - a place where things connect, and almost nothing stands on its own.

What this isn't

Not an individual assessment tool

Group-level only - no individual scoring, profiling, or monitoring. This is a deliberate design choice; see the Privacy page for the full statement. Clinical concerns need qualified practitioners.

Not proof of causation

The model surfaces statistical associations - factors that co-occur with elevated risk. Association is evidence, not causation; worker consultation explains the why.

Not a legal defence on its own

Consistent with the duty to identify psychosocial hazards, but not a substitute for the full risk management process required under WHS legislation.

Not a replacement for consultation

The data tells you where to look - not what you'll find or what to do. Structured worker consultation provides context and buy-in for change.

The methodology

Machine learning, not AI - the model is built from your data mathematically. Results are fully inspectable and reproducible - unlike black-box AI.

Complex Data Modelling: leverages the interaction effects between hazards and outcomes as required by relevant WHS legislation.

Exact, deterministic calculations - reproducible and instant regardless of model size, with no randomness between runs.

Diagnostic reasoning starts from a target and compares how other factors change in turn. Large uplifts flag statistically associated factors - evidence to investigate, not proof of causation.

Privacy

Your data never leaves your browser session. The server computes on what you send and returns results immediately. Nothing is stored, logged, or associated with you - no accounts, no analytics on queries.

Uploaded survey CSVs are processed in memory for parameter fitting, then discarded. Only the fitted model (probability tables, not raw data) returns to your browser.

All analysis in Prior Work itself is deterministic - the same data produces the same results every time.

Self-serve by design

No accounts, no data upload service, no subscription - by design. Your organisation uses its own survey platform, maintains ownership of its own data, and uses Prior Work as an analytical layer. The model lives in your browser - export it, share it, run it next year without depending on this service.

Where typical psychosocial platforms need long-term contracts, implementation projects, and data residency agreements, Prior Work is designed to be picked up and put down without a procurement cycle.

Use alongside other information

Findings are statistical patterns - a starting point, not a final answer. Output quality depends on data quality and representativeness. Prior Work surfaces uncertainty rather than hiding it. Results inform professional judgement and worker consultation; they don't replace them.

Treat this process as one layer - alongside worker consultation, observation, and other data. Default sector models are a starting point; collect your own organisation-specific data where available.

About the author

Patrick Egan is a registered, endorsed Organisational Psychologist specialising in Workplace Health and Safety.

Connect on LinkedIn.

With thanks

Prior Work owes a substantial debt to:

  • •The original People at Work team, and especially Dr Kïrsten Way, Professor Nerina Jimmieson, and Dr Chenjunyan Sun for planting the seed for this tool due to a 'happy accident' during our Masters' thesis.
  • •Jason Tangen for the inspiration to use a bring-your-own-key (BYOK) approach to intelligent report generation.
  • •Nick Ford and Nick Lewins for sharing the soft launch, being generous with several hours of feature review, and helping shape a fuller picture of the audiences this needs to serve.
  • •Hunter Dodds, Kym Lincolne, Drew Tatnell, and Amy Walker for early feedback and helping share the soft launch.

Developed with AI-assisted tooling (Claude, Anthropic) across architecture, front-end, analytics, and content. Report generation uses the Claude API with your own key.

Grounded in decades of work stress research and the broader psychosocial risk science community. No affiliation with any regulatory body.

Cite Prior Work

If you use Prior Work in research, evaluation, or publication, please cite as:

Plain text

Egan, P. (2026). Prior Work: Psychosocial risk intelligence platform (Version 1.0) [Software]. Retrieved from https://priorwork.au.

BibTeX

@software{priorwork2026,
  author    = {Egan, Patrick},
  title     = {Prior Work: Psychosocial Risk Intelligence Platform},
  year      = {2026},
  version   = {1.0},
  url       = {https://priorwork.au},
}

Contact & feedback

Bug reports, feature requests, research enquiries, accessibility issues:

[email protected]

Best-effort response times. Mark urgent accessibility issues with "Accessibility" in the subject line.