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Record W3089610294 · doi:10.2196/22847

Challenges and Strategies for Promoting Health Equity in Virtual Care: Protocol for a Scoping Review of Reviews

2020· review· en· W3089610294 on OpenAlexaffvenue
Jamie Fujioka, Suman Budhwani, Tyla Thomas-Jacques, Kristina De Vera, Priyanka Challa, Kaitlin Fuller, Sophie Hogeveen, Dara Gordon, Simone Shahid, Emily Seto, James Shaw

Bibliographic record

VenueJMIR Research Protocols · 2020
Typereview
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity Health NetworkOntario Council of University LibrariesUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsCINAHLHealth equityHealth careMEDLINESystematic reviewTelehealthInclusion (mineral)ScopusProtocol (science)PsycINFOGrey literatureMedicineMedical educationTelemedicineNursingPsychologyPsychological interventionAlternative medicinePolitical sciencePublic health

Abstract

fetched live from OpenAlex

BACKGROUND: The rapid virtualization of health services during the COVID-19 pandemic has drawn increasing attention to the impact of virtual care technologies on health equity. In some circumstances, virtual care initiatives have been shown to increase health disparities, as individuals from underserved communities are less likely to benefit from such initiatives. OBJECTIVE: The purpose of this paper is to describe a protocol for a scoping review of reviews that aims to map review-level evidence that describes challenges and strategies for promoting effective engagement with virtual care technologies among underserved communities. METHODS: Our methodology was adapted from seminal scoping review guidelines provided by Arksey and O'Malley, Levac at al, Colquhoun et al, and the Joanna Briggs Institute. Our search strategy was developed for the following databases: MEDLINE (on Ovid), EMBASE (on Ovid), CINAHL (on EBSCO), Scopus, and Epistemonikos. Supplementary searches will include the use of Google Scholar and reference tracking. Each citation will be independently screened by 2 researchers at the title and abstract level, and full-text screening will be performed in accordance with our eligibility criteria. The eligibility criteria focused on the inclusion of methods-driven reviews (ie, systematic reviews, scoping reviews, meta-analyses, realist reviews, and critical interpretative syntheses) to enhance rigor and quality. Other inclusion criteria included a focus on virtual care services that facilitate bidirectional patient-provider communication (ie, video, telephone, and asynchronous messaging visits) for underserved populations (ie, those who experience social disadvantage due to race, age, income, and other factors related to the social determinants of health). RESULTS: This scoping review of reviews will provide a broad overview of identified challenges associated with the accessibility of virtual health care services among underserved communities. In addition, strategies for improving the access to, uptake of, and engagement with virtual care technologies among underserved communities will be identified. The knowledge synthesized from this review will aid in developing and implementing virtual services that acknowledge the unique needs of populations who experience barriers to care and disproportionately worse health outcomes. The results will also inform gaps in current research. CONCLUSIONS: The rapid shift toward virtual health services has highlighted the urgent need to critically examine the intersection of virtual care and health equity. Although technology-driven innovations in health care generally aim to improve access, quality, and health outcomes, it is also possible for these innovations to produce intervention-generated inequities. Assessing current review-level evidence on the key challenges and strategies for improving the application of virtual care in underserved communities is imperative for ensuring that virtual care benefits all populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/22847.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.771
GPT teacher head0.738
Teacher spread0.033 · 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
GenreProtocol

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

Citations37
Published2020
Admission routes2
Has abstractyes

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