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Record W4390351052 · doi:10.1089/tmr.2023.0051

A Prioritized Patient-Centered Research Agenda to Reduce Disparities in Telehealth Uptake: Results from a National Consensus Conference

2023· article· en· W4390351052 on OpenAlexfundno aff
Kristin L. Rising, Mackenzie Kemp, Amy Leader, Anna Marie Chang, Andrew J Monick, Amanda Guth, Tracy Esteves Camacho, Gregory Laynor, Brooke Worster

Bibliographic record

VenueTelemedicine Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
FundersDuquesne UniversityCSL BehringPatient-Centered Outcomes Research InstituteMount Sinai Health SystemUniversity of MissouriDalhousie UniversityMassachusetts General HospitalYork UniversityGeorge Washington UniversitySchool of Medicine, New York UniversityMicrosoft
KeywordsTelehealthGeneral partnershipMedicineNursingStakeholderVotingPublic relationsMedical educationPolitical scienceFamily medicinePsychologyTelemedicineHealth care

Abstract

fetched live from OpenAlex

Introduction: We hosted a national consensus conference with a diverse group of stakeholders to develop a patient-centered research agenda focused on reducing disparities in telehealth use. Methods: = 8). Results: = 8). The top question identified by both groups focused on patient and family perspectives on important barriers to telehealth use. The entire group voting identified telehealth's impact on patient outcomes as the next most important questions, while the patient-only group identified trust-related considerations and cultural factors impacting telehealth use as next priorities. Conclusions: This project involved extensive patient and stakeholder engagement. While voting varied between patients only and the entire group of conference attendees, top identified priorities included patient and family perspectives on important barriers to telehealth, trust and cultural barriers and facilitators to telehealth, and assessment of telehealth's impact on patient outcomes. This research agenda can inform design of future research focused on addressing disparities in telehealth use.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.201
GPT teacher head0.453
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreEmpirical

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

Citations8
Published2023
Admission routes1
Has abstractyes

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