Evidence map›Paper›PMID 39280019›Full record

ArticleBiomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)2023

Investigating the Fidelity of Digital Peer Support: A Preliminary Approach using Natural Language Processing to Scale High-Fidelity Digital Peer Support.

Arya Kadakia, Sarah Masud Preum, Andrew R Bohm, Karen L Fortuna

Abstract read
In one paragraph

Article in Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Arya KadakiaBRiTE Center, Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, U.S.A.
Sarah Masud PreumDepartment of Computer Science, Dartmouth College, Hanover, U.S.A.
Andrew R BohmGeisel School of Medicine, Department of Psychiatry, Dartmouth College, Concord, U.S.A.
Karen L FortunaGeisel School of Medicine, Department of Psychiatry, Dartmouth College, Concord, U.S.A.

Funding

Peer-Delivered and Technology-Assisted Integrated Illness Management and RecoveryK01MH117496 · NIMH · DARTMOUTH-HITCHCOCK CLINIC · PI FORTUNA, KAREN L. · 2019 to 2023
$615k
NIMH NIH HHS K01 MH117496
6 · The paper itself

Abstract

Adults with serious mental illnesses are disproportionately affected by chronic health conditions that are linked to inadequately managed medical and psychiatric illnesses and are associated with poor lifestyle behaviors. Emerging intervention models emphasize the value of peer specialists (certified individuals who offer emotional, social, and practical assistance to those with similar lived experiences) in promoting better illness management and meaningful community rehabilitation. Over the last few years, there has been an increasing uptake in the use of digital services and online platforms for the dissemination of various peer services. However, current literature cannot scale current service delivery approaches through audio recording of all interactions to monitor and ensure fidelity at scale. This research aims to understand the individual components of digital peer support to develop a corpus and use natural language processing to classify high-fidelity evidence-based techniques used by peer support specialists in novel datasets. The research hypothesizes that a binary classifier can be developed with an accuracy of 70% through the analysis of digital peer support data.

Indexed as

Digital Peer SupportFidelityNatural Language ProcessingNLPPeer Support

Identifiers

PMID39280019
PMCPMC11398714

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.