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Record W4389828249 · doi:10.1080/20473869.2023.2280862

Remote support for individuals with intellectual disabilities living independently: a scoping review

2023· review· en· W4389828249 on OpenAlexafffund
Sarah Lawrason, Zach Manley, Arielle Lomness, Rachelle Hole

Bibliographic record

VenueInternational Journal of Developmental Disabilities · 2023
Typereview
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusOkanagan College
FundersAdministration for Community LivingMitacs
KeywordsIntellectual disabilityPsychologyIndependent livingActivities of daily livingDevelopmental psychologyGerontologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Objectives: The purpose of this project was to conduct a scoping review to understand factors related to remote support provision among individuals with intellectual disabilities in independent living. Methods: A systematic search was employed among eight large databases, yielding 207 articles. Following a two-phase screening process, 22 articles were included in this review. Data were charted and summarized according to types of remote support, outcomes, best practices, barriers and facilitators, and ethical considerations. Results: Overall, remote support provision was associated with positive outcomes (e.g. academic skills). Most studies used mobile apps or video self-modeling/prompting. Training for support workers facilitates use, and privacy concerns should be addressed among organizations. Discussion: Given the shift to online technology over the Covid-19 pandemic, remote support can complement in-person support when used skillfully and appropriately. Importantly, remote support should be individualized for each person. Greater research is needed using diverse study designs, assessing perceptions of support providers, and on remote support that enables live communication between users and providers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.039
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.158
GPT teacher head0.439
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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