Evidence map›Paper›PMID 42005787›Full record

ArticleHarvard data science review2022

Data Science in Public Health: Building Next Generation Capacity.

Nicholas Mirin, Heather Mattie, Latifa Jackson, Zainab Samad, Rumi Chunara

Abstract read
In one paragraph

Article in Harvard data science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Nicholas MirinDepartment of Social and Behavioral Sciences, School of Global Public Health, New York University, New York City, New York, United States of America.
Heather MattieDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, United States of America.
Latifa JacksonDepartment of Pediatrics and Child Health, College of Medicine, Howard University, Washington D.C., United States of America.
Zainab SamadDepartment of Medicine, Aga Khan University, Karachi, Sindh, Pakistan.
Rumi ChunaraDepartment of Biostatistics, School of Global Public Health, New York University, New York City, New York, United States of America.

Funding

NYU-Moi Data Science for Social Determinants Training ProgramU2RTW012121 · FIC · NEW YORK UNIVERSITY · PI CHUNARA, RUMI, MWANGI, ANN · 2021 to 2025
$1.9M
FIC NIH HHS U2R TW012121
6 · The paper itself

Abstract

Rapidly evolving technology, data, and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills, including data literacy, is particularly prominent given both the potential of novel data types and analysis methods to fill gaps in existing public health research and intervention practices, as well as the potential of such data or methods to perpetuate or augment health disparities. Through a review of public health courses and programs at the top 10 U.S. and globally ranked schools of public health, this article summarizes existing educational efforts in public health data science. These existing practices serve to inform efforts for broadening such curricula to further schools and populations. Data science ethics course offerings are also examined in context of assessing how population health principles can be blended into training across levels of data involvement to augment the traditional core of public health curricula. Parallel findings from domestic and international 'outside-the-classroom' training programs are also synthesized to advance approaches for increasing diversity in public health data science. Based on these program reviews and their synthesis, a 4-point formula is distilled for furthering public health data science education efforts toward development of a critical and inclusive mass of practitioners with fluency to leverage data to advance goals of public health and improve quality of life in the digital age.

Indexed as

curriculumdatadata scienceethicspublic health

Identifiers

PMID42005787
PMCPMC13089949

What OpenQuestion holds

Textmetadata
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.