Evidence map›Paper›PMID 39207389›Full record

ArticleJMIR medical education2024

The Digital Determinants of Health: A Guide for Competency Development in Digital Care Delivery for Health Professions Trainees.

Katharine Lawrence, Defne L Levine

Abstract read
In one paragraph

Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

2 authors.

Katharine LawrenceDepartment of Population Health, New York University Grossman School of Medicine, 227 East 30th Street 6th Floor, New York, NY, 10016, United States, 1 6465012684.ORCID 0000-0001-5640-2138
Defne L LevineDepartment of Population Health, New York University Grossman School of Medicine, 227 East 30th Street 6th Floor, New York, NY, 10016, United States, 1 6465012684.ORCID 0009-0000-5238-3144

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: Health care delivery is undergoing an accelerated period of digital transformation, spurred in part by the COVID-19 pandemic and the use of "virtual-first" care delivery models such as telemedicine. Medical education has responded to this shift with calls for improved digital health training, but there is as yet no universal understanding of the needed competencies, domains, and best practices for teaching these skills. In this paper, we argue that a "digital determinants of health" (DDoH) framework for understanding the intersections of health outcomes, technology, and training is critical to the development of comprehensive digital health competencies in medical education. Much like current social determinants of health models, the DDoH framework can be integrated into undergraduate, graduate, and professional education to guide training interventions as well as competency development and evaluation. We provide possible approaches to integrating this framework into training programs and explore priorities for future research in digitally-competent medical education.

Indexed as

COVID-19TelemedicineClinical CompetenceDelivery of Health CareEducation, MedicalHealth OccupationsHumansPandemicsSARS-CoV-2Social Determinants of Healthcompetency developmentdigital care deliverydigital determinants of healthdigital healthdigital health competenciesdigital health educationdigital health skillshealth professions trainingmedical education curriculumtraining competencies

Identifiers

PMID39207389
PMCPMC11376139

What OpenQuestion holds

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LicenceCC BY
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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.