Evidence map›Paper›PMID 39507712›Full record

ArticleFrontiers in medicine2024

Enhancing fieldwork readiness in occupational therapy students with generative AI.

Tara Mansour, John Wong

Abstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
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

2 authors.

Tara MansourMGH Institute of Health Professions, Boston, MA, United States.
John WongMGH Institute of Health Professions, Boston, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid integration of artificial intelligence (AI) into health professions education is revolutionizing traditional teaching methodologies and enhancing learning experiences. This study explores the use of generative AI to aid occupational therapy (OT) students in intervention planning. OT students often lack the background knowledge to generate a wide variety of interventions, spending excessive time on idea generation rather than clinical reasoning, practice skills, and patient care. AI can enhance creative ideation but students must still adhere to evidence-based practice, patient safety, and privacy standards. Students used ChatGPT v. 3.5 in a lecture and assignment to integrate generative AI into intervention planning. Students analyzed a case study, generated ideas with ChatGPT, selected interventions that aligned with the client's needs, and provided a rationale. They conducted evidence-based searches and wrote an analysis on how the research influenced their decisions. The results demonstrate generative AI's potential as a valuable tool for OT students, enhancing their comfort with AI and understanding of ethical and safety considerations. Qualitative feedback highlighted AI's role in boosting efficiency and creativity in intervention planning, with most students expressing strong intent to use ChatGPT in clinical practice due to its ability to reduce cognitive load and generate innovative ideas. These findings suggest that integrating generative AI into the OT curriculum could enhance intervention planning and improve clinical readiness.

Indexed as

clinical educationevidence-based practicefieldworkgenerative AIintervention planningoccupational therapy

Identifiers

PMID39507712
PMCPMC11539485

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.