LO
Care worker / nursing assistant — how we know
The page itself gives the judgements. This one gives what they rest on: which technologies bear on the work, how the estimate moved since language models reached the public, and the method behind both.
Which technologies matter here#
Four separate signals. They are deliberately not added together — a job exposed to two technologies is not twice as exposed.
How it got here#
The index is not a static number. This is where it would have sat at each capability checkpoint since ChatGPT — reconstructed, and labelled as such.
—— this stretch contains a verified event- - - no event in this stretch — reconstruction only0 = no task exposed, 100 = every task exposed
● 2 verified events for this occupation, plotted at the date it happened — the parts of the line near a marker are anchored to something checkable.
The lowest curve on this site, and the shape is almost a straight line because almost nothing in this job has a mechanism to move. What little rise there is comes from two places, neither of them the hands-on work: charting following the same ambient-documentation route as every other clinical role, and monitoring sensors arriving in facilities. Lifting and moving a person is the most demonstrated and least deployed robotics task we track, and powered hoists — which have been in wards for decades — are the precedent: they changed what a carer's back does without removing the carer. A flat curve here should not be read as security. This occupation's risks are injury, turnover and a wage set by a public budget, and a reconstruction of automation exposure cannot see any of them.
A flat line is not a forecast of safety. It says which tasks automation has reached so far — the occupations that moved least here are the ones where the constraint is physical or regulatory, and both of those can change.
Method and sources#
- Assessment date
- 2026-09-14
- Basis of the task judgements
- 2 evidence-backed · 2 platform inference · 0 not enough evidence
- Verified events
- 2