Evidence map›Paper›PMID 35919375›Full record

ArticleFrontiers in rehabilitation sciences2022

Capturing and Operationalizing Participation in Pediatric Re/Habilitation Research Using Artificial Intelligence: A Scoping Review.

Vera C Kaelin, Mina Valizadeh, Zurisadai Salgado, Julia G Sim, Dana Anaby, Andrew D Boyd, Natalie Parde, Mary A Khetani

Open access · goldAbstract readScoping Review
In one paragraph

Article in Frontiers in rehabilitation sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
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

9 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
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  6. Engagement of Preschool-Aged Children in Daily Routines.International journal of environmental research and public health · 2022
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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

8 authors at 3 institutions in 2 countries.

Vera C KaelinRehabilitation Sciences, University of Illinois at Chicago, Chicago, IL, United States.
Mina ValizadehComputer Science, University of Illinois at Chicago, Chicago, IL, United States.
Zurisadai SalgadoChildren's Participation in Environment Research Lab, University of Illinois at Chicago, Chicago, IL, United States.
Julia G SimChildren's Participation in Environment Research Lab, University of Illinois at Chicago, Chicago, IL, United States.
Dana AnabySchool of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada.
Andrew D BoydRehabilitation Sciences, University of Illinois at Chicago, Chicago, IL, United States.
Natalie PardeComputer Science, University of Illinois at Chicago, Chicago, IL, United States.
Mary A KhetaniRehabilitation Sciences, University of Illinois at Chicago, Chicago, IL, United States.
University of Illinois Chicago · USMcGill University · CAMcMaster University · CA

Funding

ACL HHS 90SFGE0032
6 · The paper itself

Abstract

Background: There is increased interest in using artificial intelligence (AI) to provide participation-focused pediatric re/habilitation. Existing reviews on the use of AI in participation-focused pediatric re/habilitation focus on interventions and do not screen articles based on their definition of participation. AI-based assessments may help reduce provider burden and can support operationalization of the construct under investigation. To extend knowledge of the landscape on AI use in participation-focused pediatric re/habilitation, a scoping review on AI-based participation-focused assessments is needed. Objective: To understand how the construct of participation is captured and operationalized in pediatric re/habilitation using AI. Methods: We conducted a scoping review of literature published in Pubmed, PsycInfo, ERIC, CINAHL, IEEE Xplore, ACM Digital Library, ProQuest Dissertation and Theses, ACL Anthology, AAAI Digital Library, and Google Scholar. Documents were screened by 2-3 independent researchers following a systematic procedure and using the following inclusion criteria: (1) focuses on capturing participation using AI; (2) includes data on children and/or youth with a congenital or acquired disability; and (3) published in English. Data from included studies were extracted [e.g., demographics, type(s) of AI used], summarized, and sorted into categories of participation-related constructs. Results: Twenty one out of 3,406 documents were included. Included assessment approaches mainly captured participation through annotated observations ( Conclusions: Main gaps for future research include lack of: (1) research reporting on common demographic factors and including samples representing the population of children and youth with a congenital or acquired disability; (2) AI-based participation assessment approaches integrating the child or youth perspective; (3) remotely administered AI-based assessment approaches capturing both child or youth attendance and involvement; and (4) AI-based assessment approaches aligning with contemporary definitions of participation.

Indexed as

assessmentcomputer visionengagementinvolvementmachine learningmeasurementnatural language processingtechnology

Identifiers

PMID35919375
PMCPMC9340801
OpenAlexW4224059745

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.