Evidence map›Paper›PMID 32117857›Full record

ArticleFrontiers in public health2020

Modernizing the Methods and Analytics Curricula for Health Science Doctoral Programs.

Ivo D Dinov

Abstract read
In one paragraph

Article in Frontiers in public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Landscape Analysis of Public Health Jobs in India to Develop an Evidence-Based Public Health Curriculum.International journal of environmental research and public health · 2022
    Article
  2. Review
  3. Article
  4. 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

1 author.

Ivo D DinovStatistics Online Computational Resource, Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI, United States.

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
Pilot and Feasibility (P and F) ProgramP30DK089503 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI RANDY J SEELEY · 2010 to 2026
$20.3M
TrainingU54EB020406 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TOGA, ARTHUR W · 2014 to 2018
$12.5M
Public Outreach and Education CoreP50NS091856 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ALBIN, ROGER L · 2014 to 2019
$10.1M
COVID-19 supplement to a computational examination of threat and reward constructs in a predominantly low-income, longitudinal sample at increased risk for internalizing disordersR01MH121079 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HYDE, LUKE WILLIAMSON, MITCHELL, COLTER M.S. · 2019 to 2024
$7.2M
Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
Pilot Projects CoreP20NR015331 · NINR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LARSON, JANET L. · 2014 to 2018
$1.3M
NCATS NIH HHS UL1 TR002240NCI NIH HHS R01 CA233487NIBIB NIH HHS U54 EB020406NIDDK NIH HHS P30 DK089503NIMH NIH HHS R01 MH121079NINDS NIH HHS P50 NS091856NINR NIH HHS P20 NR015331
6 · The paper itself

Abstract

This perspective provides a rationale for redesigning and a framework for expanding the graduate health science analytics and biomedical doctoral program curricula. It responds to digital revolution pressures, ubiquitous proliferation of big biomedical data, substantial recent advances in scientific technologies, and rapid progress in health analytics. Specifically, the paper presents a set of common prerequisites, a proposal for core computational and data analytic curriculum, and a list of expected outcome competencies for graduates of doctoral health science and biomedical programs. The manuscript emphasizes the necessity for coordinated efforts of all stakeholders, including trainees, educators, academic institutions, funding agencies, and policy makers. Concrete recommendations are presented of how to ensure graduates with terminal health science analytics and biomedical degrees are trained and able to continuously self-learn, effectively communicate across disciplines, and promote adaptation and change to counteract the relentless pace of automation and the law of diminishing returns.

Indexed as

CurriculumUniversitiesHealth Promotionanalyticsdata sciencedoctoral traininggraduate curriculahealth sciencemethodsquantitative education

Identifiers

PMID32117857
PMCPMC7031195

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.