Evidence map›Paper›PMID 32003778›Full record

ArticleAmerican journal of epidemiology2020

Perspectives on the Future of Epidemiology: A Framework for Training.

Bryan Lau, Priya Duggal, Stephan Ehrhardt, Haroutune Armenian, Charles C Branas, Graham A Colditz, Matthew P Fox, Stephen E Hawes, Jiang He, Albert Hofman and 14 more

Abstract read
In one paragraph

Article in American journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. On the Need to Revitalize Descriptive Epidemiology.American journal of epidemiology · 2022
    Article
  11. Observational
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

24 authors.

Bryan Lau
Priya Duggal
Stephan Ehrhardt
Haroutune Armenian
Charles C Branas
Graham A Colditz
Matthew P Fox
Stephen E Hawes
Jiang He
Albert Hofman
Katherine Keyes
Albert I Ko
Timothy L Lash
Deborah Levy
Michael Lu
Alfredo Morabia
Roberta Ness
F Javier Nieto
Enrique F Schisterman
Til Stürmer
Moyses Szklo
Martha Werler
Allen J Wilcox
David D Celentano

Funding

Research Mentoring and Building Capacity of underrepresented Minority Research Scientists in India.D43TW010540 · FIC · YALE UNIVERSITY · PI MICHELE BARRY, Eva Harris · 2017 to 2026
$15.5M
FIC NIH HHS D43 TW010540
6 · The paper itself

Abstract

Over the past century, the field of epidemiology has evolved and adapted to changing public health needs. Challenges include newly emerging public health concerns across broad and diverse content areas, new methods, and vast data sources. We recognize the need to engage and educate the next generation of epidemiologists and prepare them to tackle these issues of the 21st century. In this commentary, we suggest a skeleton framework upon which departments of epidemiology should build their curriculum. We propose domains that include applied epidemiology, biological and social determinants of health, communication, creativity and ability to collaborate and lead, statistical methods, and study design. We believe all students should gain skills across these domains to tackle the challenges posed to us. The aim is to train smart thinkers, not technicians, to embrace challenges and move the expanding field of epidemiology forward.

Indexed as

CurriculumForecastingEpidemiologistsEpidemiologyHumansPublic Healthapplied epidemiologybiological and social determinants of healthcommunicationepidemiologic methodsfuturestatistical methodsstudy designtraining

Identifiers

PMID32003778
PMCPMC11484601

What OpenQuestion holds

Textmetadata
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.