Evidence map›Paper›PMID 42084275›Full record

ArticleAustralian occupational therapy journal2026

Occupational therapists' perceptions of artificial intelligence: Potential, preparedness, and training needs.

Claire Del Rio, Helen Nelson, Danielle Hitch

Abstract read
In one paragraph

Article in Australian occupational therapy journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Claire Del RioWestern Health, Footscray, Victoria, Australia.
Helen NelsonWestern Health, Sunshine, Victoria, Australia.
Danielle HitchWestern Health, Sunshine, Victoria, Australia.ORCID 0000-0003-2798-2246

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence (AI) is transforming health care, yet its impact on occupational therapy remains underexplored. This study investigated occupational therapists' perceptions of AI's potential, preparedness, and training needs and differences across therapist groups in a tertiary health service.

methodsA cross-sectional online survey was conducted with 53 occupational therapists, including the Shinners Artificial Intelligence Perception tool and the AI Literacy Scale. Quantitative data were analysed descriptively and with exploratory inferential statistics, with content analysis applied to qualitative responses. CONSUMER AND COMMUNITY INVOLVEMENT: There was no consumer and community involvement in this study.

findingsParticipants reported low AI knowledge (M = 1.32), skills (M = 1.21), and confidence (M = 1.30). They acknowledged the potential for AI to enhance care but perceived limited preparedness for its implementation (M = 2.45), particularly in relation to training gaps and ethical concerns. Knowledge, skills, and confidence were strongly correlated, but service area or years of experience did not exert any significant influence. Participants wanted training about occupational therapy applications and how to use AI ethically and efficiently.

conclusionOccupational therapists are optimistic about AI but need targeted education to enhance their literacy and practice, integrated within overall workforce development strategies.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelOccupational TherapistsOccupational TherapyAdultCross-Sectional StudiesFemaleHealth Knowledge, Attitudes, PracticeHumansMaleMiddle AgedSurveys and QuestionnairesAI literacyartificial intelligencedigital healthethical use of technologyoccupational therapyworkforce preparedness

Identifiers

PMID42084275
PMCPMC13142078

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.