Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
5Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5128329320
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Tiina Kalliomäki-Levanto (openalex, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Governance: 1 paper
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
- Productivity: 1 paper
Claim outcomes
- Research Productivity: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Machine learning for workplace mental health is still mostly a lab exercise: researchers prioritize algorithmic novelty over domain-driven, causal, or deployment studies. Without interdisciplinary co‑design and randomized or quasi‑experimental evaluation linking predictions to wages, absenteeism and productivity, ML’s economic value for workplaces will remain unproven.openalex | Tiina Kalliomäki-Levanto provider id |
2026-03-09 | 1 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 1 cumulative citations. This is a coverage summary, not an author score or h-index.